**Power Transformer Diagnostics Based on Acoustic Emission Method**

Wojciech Sikorski and Krzysztof Walczak

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55211

#### **1. Introduction**

[16] D. Dyer and R.M. Stewart, Detection of rolling element bearing damage by statistical

[17] O.G. Gustafsson and T. Tallian, Detection of damage in assembled rolling bearings,

[18] Tedric A. Harris, Rolling Bearing Analysis, 4th Ed., John Wiley and Sons, Inc, USA, 2001

[19] Zhang Zhen, A Tool Condition Monitoring Approach Based on SVM, Master Thesis,

vibration analysis, J. Mech (1978):229-235

90 Acoustic Emission - Research and Applications

National University of Singapore, 2002

Trans. Am. Soc. Lubric. Eng., 5 (1962):197-205

Partial discharge (PD) diagnostics is a proven method to assess the condition of a power transformer. Too high level of PD in a transformer may quickly degrade its insulation system and lead to damage. If PDs are detected and located quickly, then the transformer may be repaired or replaced, thus preventing power outages (Bartnikas, 2002; Gulski & Smitt, 2007).

Partial discharges in power transformers in service are most often detected with DGA (Dissolved Gas Analysis) and afterwards located using acoustic emission method (AE) (Duval, 2008; Lundgaard, 1992; Bengtsson & Jönsson, 1997).

In regard to the possibility of location of defects generating partial discharges, acoustic emission is an important diagnostic method of power transformers and other HV equipment.

Widely applied techniques for the fault location based on AE method are: (i) measurement of the time difference of arrival (TDOA) of the acoustic signals, (ii) measurement of the acoustic signal amplitude in different areas of a transformer tank (standard auscultatory technique, SAT), (iii) advanced auscultatory technique (AAT), (iv) estimation of the direction of arrival (DOA) of the acoustic signal based on the phased-array signal processing (Markalous et al., 2008; Tenbohlen et al., 2010; Qing et al., 2010).

More and more frequent breakdowns of large power transformers, often ending with fire difficult to put out, compel to more critical evaluation of traditional diagnostics techniques based mostly on periodic testing. Ageing of network infrastructure causes that the possibility of insulation system damage resulting from defect developing in short period is becoming more and more real. This fact favours different kinds of monitoring systems, which, through continuous investigation of the most important transformer parameters, allow to early detection of coming damage.

© 2013 Sikorski and Walczak; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Sikorski and Walczak; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

While analysing described in the literature cases of damage of power transformers, one can observe that many of them were related to accelerated degradation of insulation system, caused by high activity of different kinds of partial discharges (Höhlein et al., 2003; Lundgaard, 2000). Therefore the PD intensity monitoring as well as monitoring of its dynamics changes in time, in selected, neuralgic points of transformer seem to be a very important indicator informing on coming damage.

model includes relation between load losses and the temperature mean of the separate bushing and tap changer position. The model was expanded on the work of the three power transformer coils as well as the relation between cooling effectiveness and number of working coolers or radiator batteries was included in the model. Basing on simulations, possibilities of a power transformer load at the current surroundings temperature are calculated every minute. In order to efficiently manage the resources, besides load and temperature analysis one can

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The prototype system for partial discharge monitoring presented in this chapter is the effect of several years of research, the results of which have already been presented, among others in (Sikorski & Walczak, 2010; Sikorski, 2012). In the mentioned literature items one can find more information on project assumptions and criteria for the selection of individual compo‐

The system works basing on the detection of acoustic emission pulses recorded by piezoelectric contact sensors (PAC WD), which are mounted on the transformer tank. A practical solution enabling easy mounting of AE sensor with a constant force to the tank is the use of special handles fitted with strong permanent magnets and such solution was used in the prototype. The pream‐ plifierisalsomountedinthehandle.Theamplifierandfiltersarelocatedinstandard19-inch,fully screened industrial housing. From the conditioning module signals are transmitted to the acquisition module. Its integral element is a powerful workstation, based on multi-core architec‐ ture, with specialized software and ultrafast acquisition card installed. Procedures for the acquisition and analysis of data are implemented in National Instrument *LabView* program‐ ming environment and realized in real time. Acquisition module, like conditioning module, was placed in a separate screened industrial housing, compatible with mechanics standard 19-inch.

The housing is waterproof and equipped with automatic temperature control system.

The system is designed for continuous, multi-month fieldwork, therefore specialized software allows not only for continuous registration of partial discharge activity, but also for correctness of the work of the system itself (e.g. temperature and humidity inside the enclosure or operation of electronic measuring circuits). The program is equipped with advanced data processing modules, which make it easier to evaluate events and noise filtering. In addition to

distinguish the following thematic groups in the monitoring system:

**3. Partial discharge online monitoring system**

**•** moisture content in oil,

**•** dissolved gas analysis,

**•** on-load tap changer,

**•** partial discharges.

nents of the system.

**•** cooling system,

**•** bushings,

Currently there are only a few commercial systems for partial discharge monitoring in the power transformer in the world. These systems are based on the method of measuring AE (*Acoustic Emission*) or UHF (*Ultra High Frequency*) signal and offer limited capabilities (Markalous et al., 2003; Rutgers et al., 2003). A drawback of these systems is that as autonomous devices they do not cooperate with superior systems, and only transmit information or alerts about the status of the unit, what makes difficult a subsequent analysis of the causes of failure and looking for correla‐ tion with other parameters recorded by the monitoring system of the transformer.

Project assumptions of the partial discharge online monitoring system, developed at the Insti‐ tute of Electric Power Engineering of Poznan University of Technology, were quite different. The system was, of course, so designed and constructed that it can work as a standalone device, what corresponds to the demand on emergency short-term monitoring (e.g. by day or a few days). However, the authors designing device have made all effort to ensure that it can be also integrat‐ edwithanysystemoffullmonitoringofthetransformer,suchase.g.MikronikaSYNDISES,which hasalreadybeeninstalledontenstransformersintheEuropeantransmissionnetworks.Through open collaboration of systems, the data collected by the PD monitoring system are visible in the superior system, so that it is possible to perform a full correlation analysis with other recorded parameters(load,voltage,oiltemperature,OLTCoperationsetc.).Thefirstprototypeimplemen‐ tation of the integrated system for PD monitoring and SYNDIS ES was performed on one of the power substations. Currently the authors have already got the annual experience related to the work of the system, what will be discussed later in the chapter.

### **2. Superior power transformers monitoring system —** *Mikronika SYNDIS ES*

In the power transformer monitoring Mikronika SYNDIS ES system, which has been installed on a few substations, the functionality of the expert system was acquired due to implementa‐ tion of the knowledge base consisting of mathematical functions and models of phenomena occurring in a power transformer. Basing on logical operations and implemented inference rules, the expert functions generate (in online mode) summary alarms, emergency signals and prompts for substation staff. Expert functions assign specific logical value to the rules and relations contained in the knowledge base. On their basis, the transformer condition is defined as *Normal*, *Warning*, *Alarm* or *Emergency*. The simulating calculations conducted in real-time are significant elements of evaluation of power transformer condition. Therefore, the special‐ ized mathematical model of thermal state was elaborated basing on the elementary relations presented in the *IEC 60076-7 – Part 7: Loading guide for oil-immersed power transformers*. The model includes relation between load losses and the temperature mean of the separate bushing and tap changer position. The model was expanded on the work of the three power transformer coils as well as the relation between cooling effectiveness and number of working coolers or radiator batteries was included in the model. Basing on simulations, possibilities of a power transformer load at the current surroundings temperature are calculated every minute. In order to efficiently manage the resources, besides load and temperature analysis one can distinguish the following thematic groups in the monitoring system:


While analysing described in the literature cases of damage of power transformers, one can observe that many of them were related to accelerated degradation of insulation system, caused by high activity of different kinds of partial discharges (Höhlein et al., 2003; Lundgaard, 2000). Therefore the PD intensity monitoring as well as monitoring of its dynamics changes in time, in selected, neuralgic points of transformer seem to be a very important indicator

Currently there are only a few commercial systems for partial discharge monitoring in the power transformer in the world. These systems are based on the method of measuring AE (*Acoustic Emission*) or UHF (*Ultra High Frequency*) signal and offer limited capabilities (Markalous et al., 2003; Rutgers et al., 2003). A drawback of these systems is that as autonomous devices they do not cooperate with superior systems, and only transmit information or alerts about the status of the unit, what makes difficult a subsequent analysis of the causes of failure and looking for correla‐

Project assumptions of the partial discharge online monitoring system, developed at the Insti‐ tute of Electric Power Engineering of Poznan University of Technology, were quite different. The system was, of course, so designed and constructed that it can work as a standalone device, what corresponds to the demand on emergency short-term monitoring (e.g. by day or a few days). However, the authors designing device have made all effort to ensure that it can be also integrat‐ edwithanysystemoffullmonitoringofthetransformer,suchase.g.MikronikaSYNDISES,which hasalreadybeeninstalledontenstransformersintheEuropeantransmissionnetworks.Through open collaboration of systems, the data collected by the PD monitoring system are visible in the superior system, so that it is possible to perform a full correlation analysis with other recorded parameters(load,voltage,oiltemperature,OLTCoperationsetc.).Thefirstprototypeimplemen‐ tation of the integrated system for PD monitoring and SYNDIS ES was performed on one of the power substations. Currently the authors have already got the annual experience related to the

**2. Superior power transformers monitoring system —** *Mikronika SYNDIS*

In the power transformer monitoring Mikronika SYNDIS ES system, which has been installed on a few substations, the functionality of the expert system was acquired due to implementa‐ tion of the knowledge base consisting of mathematical functions and models of phenomena occurring in a power transformer. Basing on logical operations and implemented inference rules, the expert functions generate (in online mode) summary alarms, emergency signals and prompts for substation staff. Expert functions assign specific logical value to the rules and relations contained in the knowledge base. On their basis, the transformer condition is defined as *Normal*, *Warning*, *Alarm* or *Emergency*. The simulating calculations conducted in real-time are significant elements of evaluation of power transformer condition. Therefore, the special‐ ized mathematical model of thermal state was elaborated basing on the elementary relations presented in the *IEC 60076-7 – Part 7: Loading guide for oil-immersed power transformers*. The

tion with other parameters recorded by the monitoring system of the transformer.

work of the system, what will be discussed later in the chapter.

informing on coming damage.

92 Acoustic Emission - Research and Applications

*ES*

**•** partial discharges.

#### **3. Partial discharge online monitoring system**

The prototype system for partial discharge monitoring presented in this chapter is the effect of several years of research, the results of which have already been presented, among others in (Sikorski & Walczak, 2010; Sikorski, 2012). In the mentioned literature items one can find more information on project assumptions and criteria for the selection of individual compo‐ nents of the system.

The system works basing on the detection of acoustic emission pulses recorded by piezoelectric contact sensors (PAC WD), which are mounted on the transformer tank. A practical solution enabling easy mounting of AE sensor with a constant force to the tank is the use of special handles fitted with strong permanent magnets and such solution was used in the prototype. The pream‐ plifierisalsomountedinthehandle.Theamplifierandfiltersarelocatedinstandard19-inch,fully screened industrial housing. From the conditioning module signals are transmitted to the acquisition module. Its integral element is a powerful workstation, based on multi-core architec‐ ture, with specialized software and ultrafast acquisition card installed. Procedures for the acquisition and analysis of data are implemented in National Instrument *LabView* program‐ ming environment and realized in real time. Acquisition module, like conditioning module, was placed in a separate screened industrial housing, compatible with mechanics standard 19-inch. The housing is waterproof and equipped with automatic temperature control system.

The system is designed for continuous, multi-month fieldwork, therefore specialized software allows not only for continuous registration of partial discharge activity, but also for correctness of the work of the system itself (e.g. temperature and humidity inside the enclosure or operation of electronic measuring circuits). The program is equipped with advanced data processing modules, which make it easier to evaluate events and noise filtering. In addition to the registration and calculation of basic PD parameters (like the number of pulses, their energy and amplitude), the program creates also event log, whose goal is to inform, with a specified frequency (service station or the superior system), about the work of the PD monitoring system or threat to the transformer resulting from the intensity discharge growth. External commu‐ nication is provided using a GSM modem (with an additional antenna) or LAN/WLAN network. The second solution was used in case of cooperation with the superior system of transformer monitoring SYNDIS ES.

**4. Partial discharge location techniques in power transformer**

Standard auscultatory technique (SAT) is one of the simplest methods of PD location. It involves the AE amplitude measurement in different areas of a transformer tank and thereby in different distance from the PD source. The SAT allows finding an area on a tank, in which the pulses of the highest amplitude/energy are recorded. One may assume that in this location under the surface of the tank, some depth in the object, the source of partial discharges' source

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The main advantages of the method are: (i) the possibility to carry out the measurements with one sensor, (ii) straightforward measurement procedure, (iii) the possibility of detection of the multi-source discharges, the occurrence of which in old transformers with aged insulation

Unfortunately, while employing the SAT method, very often one may expect errors in location of PD sources. This is because the amplitude of AE signal depends not only on the distance of a piezoelectric sensor from the discharge source (which is the basis of this measuring technique), but also depends on the energy fluctuations of partial discharges. Therefore satisfactory accuracy in the PD location with the SAT technique can be ob‐ tained only when discharges are stable (not self-extinguishing) and their energy does not change in time for the duration of the measurement. But taking into consideration that PD is a non-linear, dynamic phenomenon and has strongly stochastic character, this ideal situation is not very probable during the lengthy measurements performed on a real high voltage power transformer. The influence of small fluctuations of the PD energy on accuracy of discharges location with the use of standard auscultatory technique can easily be mitigated when one may determine the value of simple moving average (SMA) of the registered AE pulses' energy, *e*, and monitor the value of their standard deviation, *σ*. In case of fluctuations of the PD pulses' energy (e.g. their apparent charge *q* changes in a wide range, from hundreds pC to some nC), the procedure of AE-pulses energy averag‐ ing, does not give satisfactory results. The largest errors of the PD source location while employing the SAT method occur when the partial discharge activity is not-stable and after

the period of high intensity we observe their extinction for a certain time.

The proposed algorithm of the AAT method consists of the following steps:

In order to improve the efficiency and reliability of auscultatory technique, the authors propose to simultaneously monitor in each measuring point on the surface of a transform‐ er tank the simple moving average of: (i) AE waveforms energy, *SMA(e)*, and (ii) the PD apparent charge, *SMA(q)*. Additionally, the parameter *p* is introduced that is equal to the quotient of the measured values *SMA(e)* end *SMA(q)*. Due to this operation, the correct‐ ed value of the AE pulses energy depends mostly on the acoustic waves' attenuation effect, and so depends on the distance between a piezoelectric sensor and the PD source. The influence of the changes of PD energy on the result of the PD source location is then

**4.1. Standard and advanced auscultatory technique**

system is very probable (Sikorski et al., 2007, 2008, 2010).

is located.

negligible.

The schematic diagram of developed partial discharge on-line monitoring system, which was in detail described in (Sikorski & Walczak, 2010), was presented in figure 1.

**Figure 1.** Schematic diagram of developed partial discharge on-line monitoring system

#### **4. Partial discharge location techniques in power transformer**

#### **4.1. Standard and advanced auscultatory technique**

the registration and calculation of basic PD parameters (like the number of pulses, their energy and amplitude), the program creates also event log, whose goal is to inform, with a specified frequency (service station or the superior system), about the work of the PD monitoring system or threat to the transformer resulting from the intensity discharge growth. External commu‐ nication is provided using a GSM modem (with an additional antenna) or LAN/WLAN network. The second solution was used in case of cooperation with the superior system of

The schematic diagram of developed partial discharge on-line monitoring system, which was

in detail described in (Sikorski & Walczak, 2010), was presented in figure 1.

**Figure 1.** Schematic diagram of developed partial discharge on-line monitoring system

transformer monitoring SYNDIS ES.

94 Acoustic Emission - Research and Applications

Standard auscultatory technique (SAT) is one of the simplest methods of PD location. It involves the AE amplitude measurement in different areas of a transformer tank and thereby in different distance from the PD source. The SAT allows finding an area on a tank, in which the pulses of the highest amplitude/energy are recorded. One may assume that in this location under the surface of the tank, some depth in the object, the source of partial discharges' source is located.

The main advantages of the method are: (i) the possibility to carry out the measurements with one sensor, (ii) straightforward measurement procedure, (iii) the possibility of detection of the multi-source discharges, the occurrence of which in old transformers with aged insulation system is very probable (Sikorski et al., 2007, 2008, 2010).

Unfortunately, while employing the SAT method, very often one may expect errors in location of PD sources. This is because the amplitude of AE signal depends not only on the distance of a piezoelectric sensor from the discharge source (which is the basis of this measuring technique), but also depends on the energy fluctuations of partial discharges. Therefore satisfactory accuracy in the PD location with the SAT technique can be ob‐ tained only when discharges are stable (not self-extinguishing) and their energy does not change in time for the duration of the measurement. But taking into consideration that PD is a non-linear, dynamic phenomenon and has strongly stochastic character, this ideal situation is not very probable during the lengthy measurements performed on a real high voltage power transformer. The influence of small fluctuations of the PD energy on accuracy of discharges location with the use of standard auscultatory technique can easily be mitigated when one may determine the value of simple moving average (SMA) of the registered AE pulses' energy, *e*, and monitor the value of their standard deviation, *σ*. In case of fluctuations of the PD pulses' energy (e.g. their apparent charge *q* changes in a wide range, from hundreds pC to some nC), the procedure of AE-pulses energy averag‐ ing, does not give satisfactory results. The largest errors of the PD source location while employing the SAT method occur when the partial discharge activity is not-stable and after the period of high intensity we observe their extinction for a certain time.

In order to improve the efficiency and reliability of auscultatory technique, the authors propose to simultaneously monitor in each measuring point on the surface of a transform‐ er tank the simple moving average of: (i) AE waveforms energy, *SMA(e)*, and (ii) the PD apparent charge, *SMA(q)*. Additionally, the parameter *p* is introduced that is equal to the quotient of the measured values *SMA(e)* end *SMA(q)*. Due to this operation, the correct‐ ed value of the AE pulses energy depends mostly on the acoustic waves' attenuation effect, and so depends on the distance between a piezoelectric sensor and the PD source. The influence of the changes of PD energy on the result of the PD source location is then negligible.

The proposed algorithm of the AAT method consists of the following steps:

*Step 1*. Using the conventional electric method, identify the transformer phase, in which the partial discharges occur.

*Step 2*. On the transformer tank mark a grid of the measurement points, consisting of *m*-rows and *n*-columns (see Figure 2).

*Step 3*. For the given measurement point *a(i,j)*, where *i=1,…,m* and *j=1,…,n*, simultaneously register *r*-values of partial discharge apparent charge *q=(q1,q2,…,qr)* and *s* AE-waveforms X*=*[*x1*,*x2*,…,*xs*].

*Step 4*. For the registered AE waveforms [*x1*,*x2*,…,*xs*] calculate their signal energy *e=*(*e1,e2,..,es*).

*Step 5*. For the registered values of the apparent charge (*q1,q2,..,qr*) and the calculated AE waveforms energy (*e1,e2,..,es*) determine their simple moving average (SMA): *SMA(q)* and *SMA(e).*

*Step 6*. Calculate standard deviation *σ* of *SMA(e).*

*Step 7*. If *σ* ≤ 0.1 stop the acquisition, else repeat steps 3 through 6.

*Step 8*. Calculate the value of parameter *p*, which takes into account the influence of PD energy fluctuations on the energy of registered AE pulses in time for the duration of the measurements.

$$p = \frac{SMA(\varepsilon)}{SMA(q)}\tag{1}$$

**Figure 2.** Schematic diagram of AAT measurement procedure

ry technique (AAT).

**Figure 3.** Two-dimensional visualization (*Acoustic Emission Map*) of PD-source location results in advanced auscultato‐

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*Step 9*. Repeat steps 3 through 8 for all measurement points.

*Step 10*. Create matrix *P*=[*pi,j*].

*Step 11*. Create matrix *Pnorm*=[*pnormi,j*], which constitutes normalized values of matrix *P* in the range [0;1]:

$$p\_{norm\_{i\_r,j}} = \frac{(p\_{i\_r,j} \cdot p\_{min})}{(p\_{max} \cdot p\_{min})} \tag{2}$$

*Step 12*. On the base of the *Pnorm* and the bilinear interpolation function generate a high resolution intensity graph (called *Acoustic Emission Map)*.

*Step 13.* Superimpose the *Acoustic Emission Map* image on the photograph or construction drawing of the investigated transformer's phase, to find on the tank the areas which are the closest to the PD source.

Because the *Acoustic Emission Map* shows the result of PD source location on the 2D plane (see Fig. 3), it is recommended, if possible, to perform the additional measurements with the TDOA triangulation technique, by placing the AE sensors on the tank wall close to the area of highest *p* values localized with AAT.

Power Transformer Diagnostics Based on Acoustic Emission Method http://dx.doi.org/10.5772/55211 97

**Figure 2.** Schematic diagram of AAT measurement procedure

*Step 1*. Using the conventional electric method, identify the transformer phase, in which the

*Step 2*. On the transformer tank mark a grid of the measurement points, consisting of *m*-rows

*Step 3*. For the given measurement point *a(i,j)*, where *i=1,…,m* and *j=1,…,n*, simultaneously register *r*-values of partial discharge apparent charge *q=(q1,q2,…,qr)* and *s* AE-waveforms

*Step 4*. For the registered AE waveforms [*x1*,*x2*,…,*xs*] calculate their signal energy *e=*(*e1,e2,..,es*).

*Step 5*. For the registered values of the apparent charge (*q1,q2,..,qr*) and the calculated AE waveforms energy (*e1,e2,..,es*) determine their simple moving average (SMA): *SMA(q)* and

*Step 8*. Calculate the value of parameter *p*, which takes into account the influence of PD energy fluctuations on the energy of registered AE pulses in time for the duration of the

*Step 11*. Create matrix *Pnorm*=[*pnormi,j*], which constitutes normalized values of matrix *P* in the

( *pi*, *<sup>j</sup>* - *pmin*)

*Step 12*. On the base of the *Pnorm* and the bilinear interpolation function generate a high

*Step 13.* Superimpose the *Acoustic Emission Map* image on the photograph or construction drawing of the investigated transformer's phase, to find on the tank the areas which are the

Because the *Acoustic Emission Map* shows the result of PD source location on the 2D plane (see Fig. 3), it is recommended, if possible, to perform the additional measurements with the TDOA triangulation technique, by placing the AE sensors on the tank wall close to the area of highest

*SMA*(*q*) (1)

( *pmax* - *pmin*) (2)

*<sup>p</sup>* <sup>=</sup> *SMA*(*e*)

*pnormi*, *<sup>j</sup>* =

partial discharges occur.

X*=*[*x1*,*x2*,…,*xs*].

*SMA(e).*

measurements.

range [0;1]:

*Step 10*. Create matrix *P*=[*pi,j*].

closest to the PD source.

*p* values localized with AAT.

and *n*-columns (see Figure 2).

96 Acoustic Emission - Research and Applications

*Step 6*. Calculate standard deviation *σ* of *SMA(e).*

*Step 7*. If *σ* ≤ 0.1 stop the acquisition, else repeat steps 3 through 6.

*Step 9*. Repeat steps 3 through 8 for all measurement points.

resolution intensity graph (called *Acoustic Emission Map)*.

**Figure 3.** Two-dimensional visualization (*Acoustic Emission Map*) of PD-source location results in advanced auscultato‐ ry technique (AAT).

The most important modification, comparing to the SAT method, is application of the parameter *p* which, to a very significant degree, minimizes the negative influence of the temporal changes of PD energy on the defect location results. This positive feature is illustrated by a simulation shown in figure 4. For simplification, it was assumed that the defect is present in the 'B' phase of the transformer, and the AE pulses were registered only in 7 measuring points. In the first case, it was assumed that the partial discharges are stable and their energy does not change in time for the duration of the measurements. Of course, with such an idealistic and almost unrealistic assumption, both techniques achieve identical and correct result of the defect location (Fig. 4a). As for the second analysed case, when energy of PD varies (fluctuate) during the acoustic emission signals' measurements, only the AAT technique allows to obtain the proper location of the defect (Fig. 4b).

Due to a low sensitivity of the PD detection procedure using acoustic emission method, the AAT method is the best for location of the defects that are the source of discharges with high energy (e.g. surface and creeping discharges, sparks), or defects that are close to a transformer tank (e.g. discharges in bushing and near the winding at the bushing connection, on the surface of outer pressboard barriers and spacers, etc.). Unfortunately, location of the internal PD sources (e.g. within the winding), is very difficult or even impossible. It concerns not only the

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Furthermore, it should be stressed, that complex and non-homogeneous internal construction of the transformer (pressboard barriers, supporting beams made of wood or phenolic resin etc.) and transformer tank (corrugated walls, magnetic or non-magnetic shields, stiffeners, gussets or ribs reinforcing the mechanical strength, welds etc.) impedes a proper interpretation

The PD source location based on TDOA technique is usually applied during on-site diagnostic tests of large power transformers. At least four AE sensors are used for spatial location of a defect *PD(x*, *y*, *z)* in transformer tank. The sensors theoretically are fixed in different distances from the PD source (Fig. 5). The position of the defect is estimated basing on measured time difference of arrival of acoustic signals. In order to find the coordinates of defect one should

where: x, y, z – unknown PD-source coordinates in space, T – unknown acoustic wave propagation time from PD-source to the nearest sensor numbered as S1, xS1..4, yS1...4, zS1…4 - Cartesian coordinates of the four AE sensors S1...S4, t12, t13, t14 - propagation time delay between the sensor 1 and the sensors 2, 3 and 4 respectively (t12 < t13 < t14), *voil* – acoustic wave propagation

This nonlinear system of equations can be solved with one of direct (non-iterative) solver algorithms or with a least square iterative algorithm, which efficiency strongly depends on

The most common errors in accurate location of PD source coordinates using TDOA technique

velocity in transformer oil (1413 m/s at 20ºC, 1300 m/s at 50ºC, 1200 m/s at 80ºC).

(*x* - *xs*1)<sup>2</sup> + (*y* - *ys*1)<sup>2</sup> + (*z* - *zs*1)<sup>2</sup> =(*νoil* ∙*T* )<sup>2</sup> (3)

(*x* - *xs*2)<sup>2</sup> + (*y* - *ys*2)<sup>2</sup> + (*z* - *zs*2)<sup>2</sup> =(*νoil* ∙(*T* + *t*12))<sup>2</sup> (4)

(*x* - *xs*3)<sup>2</sup> + (*y* - *ys*3)<sup>2</sup> + (*z* - *zs*3)<sup>2</sup> =(*νoil* ∙(*T* + *t*13))<sup>2</sup> (5)

(*x* - *xs*4)<sup>2</sup> + (*y* - *ys*4)<sup>2</sup> + (*z* - *zs*4)<sup>2</sup> =(*νoil* ∙(*T* + *t*14))<sup>2</sup> (6)

use of AAT method, but any other technique that is based on acoustic emission.

of the AAT results because it causes a strong suppression of the acoustic signal.

**4.2. Time Difference of Arrival (TDOA) technique**

solve the following nonlinear system of equations:

initial values selected by user.

in large power transformers result from:

The on-site PD measurement using a standard IEC-60270 PD detector is complicated. There‐ fore, the new AAT method is dedicated mainly for the transformer manufacturing plants and the repair companies. However, modern PD-detectors with the integrated noise-gating channel for noise-suppression via an external antenna and the software for noise reduction and filtering may also expand the AAT usage to transformers installed at substation (Kraetge et al., 2010).

**Figure 4.** The diagram illustrating the result of PD location employing the parameter SMA(e) (standard auscultatory technique) and parameter p (advanced auscultatory technique) in case when the apparent charge of partial discharg‐ es is: a) stable, and b) varying in time during the measurement.

Due to a low sensitivity of the PD detection procedure using acoustic emission method, the AAT method is the best for location of the defects that are the source of discharges with high energy (e.g. surface and creeping discharges, sparks), or defects that are close to a transformer tank (e.g. discharges in bushing and near the winding at the bushing connection, on the surface of outer pressboard barriers and spacers, etc.). Unfortunately, location of the internal PD sources (e.g. within the winding), is very difficult or even impossible. It concerns not only the use of AAT method, but any other technique that is based on acoustic emission.

Furthermore, it should be stressed, that complex and non-homogeneous internal construction of the transformer (pressboard barriers, supporting beams made of wood or phenolic resin etc.) and transformer tank (corrugated walls, magnetic or non-magnetic shields, stiffeners, gussets or ribs reinforcing the mechanical strength, welds etc.) impedes a proper interpretation of the AAT results because it causes a strong suppression of the acoustic signal.

#### **4.2. Time Difference of Arrival (TDOA) technique**

The most important modification, comparing to the SAT method, is application of the parameter *p* which, to a very significant degree, minimizes the negative influence of the temporal changes of PD energy on the defect location results. This positive feature is illustrated by a simulation shown in figure 4. For simplification, it was assumed that the defect is present in the 'B' phase of the transformer, and the AE pulses were registered only in 7 measuring points. In the first case, it was assumed that the partial discharges are stable and their energy does not change in time for the duration of the measurements. Of course, with such an idealistic and almost unrealistic assumption, both techniques achieve identical and correct result of the defect location (Fig. 4a). As for the second analysed case, when energy of PD varies (fluctuate) during the acoustic emission signals' measurements, only the AAT technique allows to obtain

The on-site PD measurement using a standard IEC-60270 PD detector is complicated. There‐ fore, the new AAT method is dedicated mainly for the transformer manufacturing plants and the repair companies. However, modern PD-detectors with the integrated noise-gating channel for noise-suppression via an external antenna and the software for noise reduction and filtering may

**Figure 4.** The diagram illustrating the result of PD location employing the parameter SMA(e) (standard auscultatory technique) and parameter p (advanced auscultatory technique) in case when the apparent charge of partial discharg‐

es is: a) stable, and b) varying in time during the measurement.

also expand the AAT usage to transformers installed at substation (Kraetge et al., 2010).

the proper location of the defect (Fig. 4b).

98 Acoustic Emission - Research and Applications

The PD source location based on TDOA technique is usually applied during on-site diagnostic tests of large power transformers. At least four AE sensors are used for spatial location of a defect *PD(x*, *y*, *z)* in transformer tank. The sensors theoretically are fixed in different distances from the PD source (Fig. 5). The position of the defect is estimated basing on measured time difference of arrival of acoustic signals. In order to find the coordinates of defect one should solve the following nonlinear system of equations:

$$(\mathbf{x} \cdot \mathbf{z}\_{s1})^2 + (y \cdot y\_{s1})^2 + (z \cdot z\_{s1})^2 = (\nu\_{oil} \bullet T)^2 \tag{3}$$

$$(\mathbf{x} \cdot \mathbf{z}\_{s2})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s2})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s2})^2 = (\mathbf{v}\_{oil} \bullet (T + t\_{12}))^2 \tag{4}$$

$$(\mathbf{x} \cdot \mathbf{z}\_{s3})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s3})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s3})^2 = (\mathbf{v}\_{oil} \bullet \mathbf{(}\mathbf{T} + \mathbf{t}\_{13}))^2 \tag{5}$$

$$(\mathbf{x} \cdot \mathbf{z}\_{s4})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s4})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s4})^2 = (\mathbf{v}\_{oil} \bullet \{\mathbf{T} + \mathbf{t}\_{14}\})^2 \tag{6}$$

where: x, y, z – unknown PD-source coordinates in space, T – unknown acoustic wave propagation time from PD-source to the nearest sensor numbered as S1, xS1..4, yS1...4, zS1…4 - Cartesian coordinates of the four AE sensors S1...S4, t12, t13, t14 - propagation time delay between the sensor 1 and the sensors 2, 3 and 4 respectively (t12 < t13 < t14), *voil* – acoustic wave propagation velocity in transformer oil (1413 m/s at 20ºC, 1300 m/s at 50ºC, 1200 m/s at 80ºC).

This nonlinear system of equations can be solved with one of direct (non-iterative) solver algorithms or with a least square iterative algorithm, which efficiency strongly depends on initial values selected by user.

The most common errors in accurate location of PD source coordinates using TDOA technique in large power transformers result from:

**•** simplifying assumption that the acoustic wave propagates only in oil with the velocity *voil* < 1500 m/s. This ignores the fact that acoustic wave propagates in transformer tank wall with velocity 5100 m/s as well.

In most cases, when signal-to-noise ratio (SNR) is high, the auto-pickers allow to estimate timeof-arrival with satisfying accuracy. In the rest of cases, it is necessary to apply additional

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The figure 6 presents the example of the time-of-arrival estimation of partial discharge pulse

**Figure 6.** Exemplary estimation of the time-of-arrival of partial discharge pulse based on AIC and energy criterion

The application of triggering with an electrical (IEC-60270 detector, only in laboratory conditions) or an electromagnetic (RFCT or UHF sensors, both in laboratory and on-site conditions) partial discharge signal is another variant of defects location technique (Fig. 7). The main advantages of simultaneous use of electrical/electromagnetic triggering and acoustic emission method are: (i) obtaining information on time of partial discharge initiation and (ii) reduction in the number of AE sensors required for measurement procedure (three are sufficient). In order to locate PD-source the following system of equations should be solved:

(*x* - *xs*1)<sup>2</sup> + (*y* - *ys*1)<sup>2</sup> + (*z* - *zs*1)<sup>2</sup> =(*νoil* ∙*t*1)<sup>2</sup> (7)

advanced signal denoising methods, which increase the SNR.

based on AIC and energy criterion.


Time-of-arrival of partial discharge pulses is usually estimated by an experienced expert. It is also possible to apply an algorithm dedicated for automatic time-of-arrival estimation (socalled auto-picker). Currently used algorithms giving satisfying results base on the following criteria: (i) Signal Energy (EC), (ii) Akaike Information Criterion (AIC), (iii) Discrete Wavelet Decomposition (DWT), (iv) Gabor centroid, (v) Maximum Likelihood (ML), (vi) Phase in frequency domain and (vii) trigger level.

**Figure 5.** Schematic diagram of Time Difference of Arrival (TDOA) technique for partial discharge location in power transformer

In most cases, when signal-to-noise ratio (SNR) is high, the auto-pickers allow to estimate timeof-arrival with satisfying accuracy. In the rest of cases, it is necessary to apply additional advanced signal denoising methods, which increase the SNR.

**•** simplifying assumption that the acoustic wave propagates only in oil with the velocity *voil* < 1500 m/s. This ignores the fact that acoustic wave propagates in transformer tank wall with

**•** incorrect time-of-arrival estimation of signal propagating along the shortest geometric path. In regard to the fact that the velocity in metal is greater than in oil, the acoustic wave, which most of its way travels in tank wall (structure-borne path), arrives at the sensor first. Afterwards the sensor registers the wave, which propagated in oil slowly (direct acoustic

**•** inaccurate measurement of coordinates of the AE sensors as a result of complex transformer

Time-of-arrival of partial discharge pulses is usually estimated by an experienced expert. It is also possible to apply an algorithm dedicated for automatic time-of-arrival estimation (socalled auto-picker). Currently used algorithms giving satisfying results base on the following criteria: (i) Signal Energy (EC), (ii) Akaike Information Criterion (AIC), (iii) Discrete Wavelet Decomposition (DWT), (iv) Gabor centroid, (v) Maximum Likelihood (ML), (vi) Phase in

**Figure 5.** Schematic diagram of Time Difference of Arrival (TDOA) technique for partial discharge location in power

velocity 5100 m/s as well.

100 Acoustic Emission - Research and Applications

frequency domain and (vii) trigger level.

path).

transformer

tank structure.

The figure 6 presents the example of the time-of-arrival estimation of partial discharge pulse based on AIC and energy criterion.

**Figure 6.** Exemplary estimation of the time-of-arrival of partial discharge pulse based on AIC and energy criterion

The application of triggering with an electrical (IEC-60270 detector, only in laboratory conditions) or an electromagnetic (RFCT or UHF sensors, both in laboratory and on-site conditions) partial discharge signal is another variant of defects location technique (Fig. 7). The main advantages of simultaneous use of electrical/electromagnetic triggering and acoustic emission method are: (i) obtaining information on time of partial discharge initiation and (ii) reduction in the number of AE sensors required for measurement procedure (three are sufficient). In order to locate PD-source the following system of equations should be solved:

$$(\mathbf{x} \cdot \mathbf{z}\_{s1})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s1})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s1})^2 = (\mathbf{v}\_{oil} \bullet \mathbf{t}\_1)^2 \tag{7}$$

$$(\mathbf{x} \cdot \mathbf{z}\_{s1})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s1})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s1})^2 = (\mathbf{v}\_{\text{oil}} \bullet \mathbf{t}\_2)^2 \tag{8}$$

**Parameter Value**

The main reason for performing the partial discharge investigation was a disturbing level of flammable gases in the insulating oil, especially hydrogen. It was noticed just after a flashover which occurred in 2002 in a distribution line that caused a flow of the short-circuit current in the local power system. In successive years the periodic diagnostic measurements revealed a continuous increase of the amount of gases dissolved in the oil. In 2008 a sudden increase of gases in the oil was noticed. The amount of hydrogen exceeded the level of 2000 ppm, and the breakdown voltage of oil decreased to 18 kV, while the permissible value is not less than 50

Unfortunately, even after oil treatment process, continuous increase of flammable gases in oil content was still observed. In 2009 SFRA (Sweep Frequency Response Analysis) investigation was made, and the results suggested that the axial displacement, as well as the radial buckling of low voltage and compensating winding was probable. In April 2011 the concentration of hydrogen exceeded 2200 ppm (with permissible value of 350 ppm), and content of CO2

In order to estimate the danger of a transformer failure, the owner decided to make additional measurements of PD using the electrical method. For that reason, the 220/110 kV transmission overhead lines connected to this transformer had to be temporarily switched off. It should be noted that due to very intensive interference originating mainly from the corona on the transmission lines, it was not possible to detect the PD with the use of the conventional electrical method according to IEC 60270. However in this case, the substation was equipped with one transformer only, so when the transformer and the transmission lines were deenergized for the PD measurement system calibration, the interference did not exceed the level of 300 pC. Next, during the PD measurement procedure, when the transformer and the lines were switched on, the interference level changed from 400 pC (110 kV side) to maximum 8 nC (220 kV side), depending on investigated phase and transformer load. Measurements with the electrical method were done for all phases of the transformer (on 110 kV and 220 kV side) using

The measurement procedure was repeated for each transformer phase and consisted of:

**1.** Disconnection of transmission line and switching the transformer off,

**4.** Energization of the transformer and detection of the partial discharges.

**2.** Connection of the measuring impedance to the measuring tap of a bushing,

**3.** Calibration of the measuring system with the use of a standard PD calibrator,

exceeded 3100 ppm, approaching the permissible value equal to 4000 ppm.

**Table 1.** Main parameters of investigated transformer

kV for this type of transformer.

the measuring taps of the bushings.

Type RTdxP 125000/220 Voltage 230/120/10.5 kV Power 160/160/50 MVA

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$$(\mathbf{x} \cdot \mathbf{z}\_{s1})^2 + (\mathbf{y} \cdot \mathbf{y}\_{s1})^2 + (\mathbf{z} \cdot \mathbf{z}\_{s1})^2 = (\mathbf{v}\_{oil} \bullet \mathbf{t}\_3)^2 \tag{9}$$

where: x, y, z – unknown PD-source coordinates in space, xS1..3, yS1...3, zS1…3 - Cartesian coordi‐ nates of the sensors S1...S3, t1, t2, t3 – measured absolute arrival times, *voil* – acoustic wave propagation velocity in transformer oil.

**Figure 7.** Schematic diagram of Time Difference of Arrival (TDOA) technique with electrical/electromagnetic trigger‐ ing for partial discharge location in power transformer

#### **5. Examples of partial discharge location and on-line monitoring**

#### **5.1.** *Case study 1* **— Short-term monitoring (daily) of the 160 MVA transformer**

Investigations were carried out in a power transformer 125000/220 manufactured in 1978 with the parameters shown in Table 1.


**Table 1.** Main parameters of investigated transformer

(*x* - *xs*1)<sup>2</sup> + (*y* - *ys*1)<sup>2</sup> + (*z* - *zs*1)<sup>2</sup> =(*νoil* ∙*t*2)<sup>2</sup> (8)

(*x* - *xs*1)<sup>2</sup> + (*y* - *ys*1)<sup>2</sup> + (*z* - *zs*1)<sup>2</sup> =(*νoil* ∙*t*3)<sup>2</sup> (9)

where: x, y, z – unknown PD-source coordinates in space, xS1..3, yS1...3, zS1…3 - Cartesian coordi‐ nates of the sensors S1...S3, t1, t2, t3 – measured absolute arrival times, *voil* – acoustic wave

**Figure 7.** Schematic diagram of Time Difference of Arrival (TDOA) technique with electrical/electromagnetic trigger‐

Investigations were carried out in a power transformer 125000/220 manufactured in 1978 with

**5. Examples of partial discharge location and on-line monitoring**

**5.1.** *Case study 1* **— Short-term monitoring (daily) of the 160 MVA transformer**

propagation velocity in transformer oil.

102 Acoustic Emission - Research and Applications

ing for partial discharge location in power transformer

the parameters shown in Table 1.

The main reason for performing the partial discharge investigation was a disturbing level of flammable gases in the insulating oil, especially hydrogen. It was noticed just after a flashover which occurred in 2002 in a distribution line that caused a flow of the short-circuit current in the local power system. In successive years the periodic diagnostic measurements revealed a continuous increase of the amount of gases dissolved in the oil. In 2008 a sudden increase of gases in the oil was noticed. The amount of hydrogen exceeded the level of 2000 ppm, and the breakdown voltage of oil decreased to 18 kV, while the permissible value is not less than 50 kV for this type of transformer.

Unfortunately, even after oil treatment process, continuous increase of flammable gases in oil content was still observed. In 2009 SFRA (Sweep Frequency Response Analysis) investigation was made, and the results suggested that the axial displacement, as well as the radial buckling of low voltage and compensating winding was probable. In April 2011 the concentration of hydrogen exceeded 2200 ppm (with permissible value of 350 ppm), and content of CO2 exceeded 3100 ppm, approaching the permissible value equal to 4000 ppm.

In order to estimate the danger of a transformer failure, the owner decided to make additional measurements of PD using the electrical method. For that reason, the 220/110 kV transmission overhead lines connected to this transformer had to be temporarily switched off. It should be noted that due to very intensive interference originating mainly from the corona on the transmission lines, it was not possible to detect the PD with the use of the conventional electrical method according to IEC 60270. However in this case, the substation was equipped with one transformer only, so when the transformer and the transmission lines were deenergized for the PD measurement system calibration, the interference did not exceed the level of 300 pC. Next, during the PD measurement procedure, when the transformer and the lines were switched on, the interference level changed from 400 pC (110 kV side) to maximum 8 nC (220 kV side), depending on investigated phase and transformer load. Measurements with the electrical method were done for all phases of the transformer (on 110 kV and 220 kV side) using the measuring taps of the bushings.

The measurement procedure was repeated for each transformer phase and consisted of:



In case of the AAT, in the first step, the measurement points on the surface of transformer tank were chosen and marked. These points formed a measurement grid. In order to increase the reliability of measurements, and simplify the interpretation of the obtained results, the fragments of tank walls with higher thickness were omitted (e.g. corrugated walls and welds).

> (a) (b)

Partial<\$%&?>discharge<\$%&?>(PD)<\$%&?>diagnostics<\$%&?>is<\$%&?>a<\$%&?>proven<\$%&?>method<\$%&?>to<\$%&?>assess< \$%&?>the<\$%&?>condition<\$%&?>of<\$%&?>a<\$%&?>power<\$%&?>transformer.<\$%&?>Too<\$%&?>high<\$%&?>level<\$%&?>of< \$%&?>PD<\$%&?>in<\$%&?>a<\$%&?>transformer<\$%&?>may<\$%&?>quickly<\$%&?>degrade<\$%&?>its<\$%&?>insulation<\$%&?>s ystem<\$%&?>and<\$%&?>lead<\$%&?>to<\$%&?>damage.<\$%&?>If<\$%&?>PDs<\$%&?>are<\$%&?>detected<\$%&?>and<\$%&?>locat ed<\$%&?>quickly,<\$%&?>then<\$%&?>the<\$%&?>transformer<\$%&?>may<\$%&?>be<\$%&?>repaired<\$%&?>or<\$%&?>replaced,<\$ %&?>thus<\$%&?>preventing<\$%&?>power<\$%&?>outages<\$%&?>(Bartnikas,<\$%&?>2002;<\$%&?>Gulski<\$%&?>&<\$%&?>Smitt,<

On the basis of the results obtained with the use of AAT, the *Acoustic Emission Map* was prepared and superimposed on a photograph of the transformer tank. The analysis of the *Acoustic Emission Map* image showed that in the HV phase 2 two sources of partial discharges

**Figure 9.** The measurement grid (36 points) used for PD source location with advanced auscultatory technique (a) and the result of PD source location presented as an *Acoustic Emission Map* applied in the picture of the *HV 2* phase of the

Partial<\$%&?>discharges<\$%&?>in<\$%&?>power<\$%&?>transformers<\$%&?>in<\$%&?>service<\$%&?>are<\$%&?>most<\$%&?>oft en<\$%&?>detected<\$%&?>with<\$%&?>DGA<\$%&?>(Dissolved<\$%&?>Gas<\$%&?>Analysis)<\$%&?>and<\$%&?>afterwards<\$%&? >located<\$%&?>using<\$%&?>acoustic<\$%&?>emission<\$%&?>method<\$%&?>(AE)<\$%&?>(Duval,<\$%&?>2008;<\$%&?>Lundgaar

When the acoustic emission measurements with the AAT were finished, a procedure of the PD sources location was initiated with the use of a triangulation technique. The AE sensors were placed on the tank wall in the locations identified by the *Acoustic Emission Map* image analysis. Placing the sensors in region of the strongest AE signals was done to increase the precision of XYZ coordinates' estimation of the PD source location using the triangulation

In<\$%&?>regard<\$%&?>to<\$%&?>the<\$%&?>possibility<\$%&?>of<\$%&?>location<\$%&?>of<\$%&?>defects<\$%&?>generating<\$% &?>partial<\$%&?>discharges,<\$%&?>acoustic<\$%&?>emission<\$%&?>is<\$%&?>an<\$%&?>important<\$%&?>diagnostic<\$%&?>met hod<\$%&?>of<\$%&?>power<\$%&?>transformers<\$%&?>and<\$%&?>other<\$%&?>HV<\$%&?>equipment.<\$%&?><\$%&?>

Widely<\$%&?>applied<\$%&?>techniques<\$%&?>for<\$%&?>the<\$%&?>fault<\$%&?>location<\$%&?>based<\$%&?>on<\$%&?>AE<\$ %&?>method<\$%&?>are:<\$%&?>(i)<\$%&?>measurement<\$%&?>of<\$%&?>the<\$%&?>time<\$%&?>difference<\$%&?>of<\$%&?>arri val<\$%&?>(TDOA)<\$%&?>of<\$%&?>the<\$%&?>acoustic<\$%&?>signals,<\$%&?>(ii)<\$%&?>measurement<\$%&?>of<\$%&?>the<\$% &?>acoustic<\$%&?>signal<\$%&?>amplitude<\$%&?>in<\$%&?>different<\$%&?>areas<\$%&?>of<\$%&?>a<\$%&?>transformer<\$%&? >tank<\$%&?>(standard<\$%&?>auscultatory<\$%&?>technique,<\$%&?>SAT),<\$%&?>(iii)<\$%&?>advanced<\$%&?>auscultatory<\$% &?>technique<\$%&?>(AAT),<\$%&?>(iv)<\$%&?>estimation<\$%&?>of<\$%&?>the<\$%&?>direction<\$%&?>of<\$%&?>arrival<\$%&?>(

winding leads or in the support beam that is close to the transformer tank.

The analysis of the results of PD source location, obtained with the triangulation method showed that both sources of discharges were placed near the symmetry axis of the phase *HV 2* bushing and the transformer tank (Fig. 10a and 10b). On the basis of the investigation results, a hypothesis was assumed that partial discharges were generated inside the insulation of the

**Power<\$%&?>Transformer<\$%&?>Diagnostics<\$%&?>Based<\$%&?>on<\$%&?>Acoustic<\$%&?**

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105

The measurement grid consisted of 36 points, as it is shown in figure 9a.

**>Emission<\$%&?>Method** 

**1.<\$%&?>Introduction** 

investigated power transformer (b).

were present (Fig. 9b).

\$%&?>2007).<\$%&?>

method.

Poland

Wojciech<\$%&?>Sikorski<\$%&?>and<\$%&?>Krzysztof<\$%&?>Walczak

d,<\$%&?>1992;<\$%&?>Bengtsson<\$%&?>&<\$%&?>Jönsson,<\$%&?>1997).

Poznan<\$%&?>University<\$%&?>of<\$%&?>Technology

**Table 2.** Maximum value of PD apparent charge registered during test

The result of the PD measurements carried out with conventional electrical method revealed the presence of strong discharges in phase *HV 2* (Table 2). The maximum value of apparent charge reached 17 nC (Fig. 8), however the range of a phase angle, in which the discharges appeared, was mainly from 30° to 90°. In other phases of 220 kV side (*HV 1* and *HV 3*) the PD pulses were also recorded, but their apparent charge value did not exceed 10-11 nC. The range of phase angle was identical as in *HV 2* phase. On the basis of the obtained results it was concluded that the signals observed in phases *HV 1* and *HV 3* were the same as those coming from the *HV 2*, but attenuated, indicating their origin as *HV 2*. In the case of 110 kV side, only the low-energy signals were registered with the apparent charge up to 1 nC, and in *LV 2* phase only.

**Figure 8.** The results of PD apparent charge measurement in phase *HV 2* of investigated transformer.

On the basis of the results obtained with the use of conventional electrical method, it was decided that the procedure of PD source location should be restricted to *HV 2* phase only.

The time of investigation was not limited, as well as it was possible to carry out the continuous monitoring of the apparent charge level. It was also possible to perform the PD location using both the AAT and the TDOA triangulation.

In case of the AAT, in the first step, the measurement points on the surface of transformer tank were chosen and marked. These points formed a measurement grid. In order to increase the reliability of measurements, and simplify the interpretation of the obtained results, the fragments of tank walls with higher thickness were omitted (e.g. corrugated walls and welds). The measurement grid consisted of 36 points, as it is shown in figure 9a. Wojciech<\$%&?>Sikorski<\$%&?>and<\$%&?>Krzysztof<\$%&?>Walczak Poznan<\$%&?>University<\$%&?>of<\$%&?>Technology Poland

**>Emission<\$%&?>Method** 

**Transformer phase Apparent charge [nC]** HV 1 10 HV 2 17 HV 3 11 LV 1 N/A\* LV 2 1 LV 3 N/A\*

The result of the PD measurements carried out with conventional electrical method revealed the presence of strong discharges in phase *HV 2* (Table 2). The maximum value of apparent charge reached 17 nC (Fig. 8), however the range of a phase angle, in which the discharges appeared, was mainly from 30° to 90°. In other phases of 220 kV side (*HV 1* and *HV 3*) the PD pulses were also recorded, but their apparent charge value did not exceed 10-11 nC. The range of phase angle was identical as in *HV 2* phase. On the basis of the obtained results it was concluded that the signals observed in phases *HV 1* and *HV 3* were the same as those coming from the *HV 2*, but attenuated, indicating their origin as *HV 2*. In the case of 110 kV side, only the low-energy signals were registered with the

\* No PD activities or PD buried in background noise

104 Acoustic Emission - Research and Applications

**Table 2.** Maximum value of PD apparent charge registered during test

apparent charge up to 1 nC, and in *LV 2* phase only.

both the AAT and the TDOA triangulation.

**Figure 8.** The results of PD apparent charge measurement in phase *HV 2* of investigated transformer.

On the basis of the results obtained with the use of conventional electrical method, it was decided that the procedure of PD source location should be restricted to *HV 2* phase only.

The time of investigation was not limited, as well as it was possible to carry out the continuous monitoring of the apparent charge level. It was also possible to perform the PD location using

**1.<\$%&?>Introduction**  Partial<\$%&?>discharge<\$%&?>(PD)<\$%&?>diagnostics<\$%&?>is<\$%&?>a<\$%&?>proven<\$%&?>method<\$%&?>to<\$%&?>assess< **Figure 9.** The measurement grid (36 points) used for PD source location with advanced auscultatory technique (a) and the result of PD source location presented as an *Acoustic Emission Map* applied in the picture of the *HV 2* phase of the investigated power transformer (b).

\$%&?>the<\$%&?>condition<\$%&?>of<\$%&?>a<\$%&?>power<\$%&?>transformer.<\$%&?>Too<\$%&?>high<\$%&?>level<\$%&?>of< \$%&?>PD<\$%&?>in<\$%&?>a<\$%&?>transformer<\$%&?>may<\$%&?>quickly<\$%&?>degrade<\$%&?>its<\$%&?>insulation<\$%&?>s ystem<\$%&?>and<\$%&?>lead<\$%&?>to<\$%&?>damage.<\$%&?>If<\$%&?>PDs<\$%&?>are<\$%&?>detected<\$%&?>and<\$%&?>locat ed<\$%&?>quickly,<\$%&?>then<\$%&?>the<\$%&?>transformer<\$%&?>may<\$%&?>be<\$%&?>repaired<\$%&?>or<\$%&?>replaced,<\$ %&?>thus<\$%&?>preventing<\$%&?>power<\$%&?>outages<\$%&?>(Bartnikas,<\$%&?>2002;<\$%&?>Gulski<\$%&?>&<\$%&?>Smitt,< \$%&?>2007).<\$%&?> On the basis of the results obtained with the use of AAT, the *Acoustic Emission Map* was prepared and superimposed on a photograph of the transformer tank. The analysis of the *Acoustic Emission Map* image showed that in the HV phase 2 two sources of partial discharges were present (Fig. 9b).

Partial<\$%&?>discharges<\$%&?>in<\$%&?>power<\$%&?>transformers<\$%&?>in<\$%&?>service<\$%&?>are<\$%&?>most<\$%&?>oft en<\$%&?>detected<\$%&?>with<\$%&?>DGA<\$%&?>(Dissolved<\$%&?>Gas<\$%&?>Analysis)<\$%&?>and<\$%&?>afterwards<\$%&? >located<\$%&?>using<\$%&?>acoustic<\$%&?>emission<\$%&?>method<\$%&?>(AE)<\$%&?>(Duval,<\$%&?>2008;<\$%&?>Lundgaar d,<\$%&?>1992;<\$%&?>Bengtsson<\$%&?>&<\$%&?>Jönsson,<\$%&?>1997). In<\$%&?>regard<\$%&?>to<\$%&?>the<\$%&?>possibility<\$%&?>of<\$%&?>location<\$%&?>of<\$%&?>defects<\$%&?>generating<\$% &?>partial<\$%&?>discharges,<\$%&?>acoustic<\$%&?>emission<\$%&?>is<\$%&?>an<\$%&?>important<\$%&?>diagnostic<\$%&?>met hod<\$%&?>of<\$%&?>power<\$%&?>transformers<\$%&?>and<\$%&?>other<\$%&?>HV<\$%&?>equipment.<\$%&?><\$%&?> When the acoustic emission measurements with the AAT were finished, a procedure of the PD sources location was initiated with the use of a triangulation technique. The AE sensors were placed on the tank wall in the locations identified by the *Acoustic Emission Map* image analysis. Placing the sensors in region of the strongest AE signals was done to increase the precision of XYZ coordinates' estimation of the PD source location using the triangulation method.

Widely<\$%&?>applied<\$%&?>techniques<\$%&?>for<\$%&?>the<\$%&?>fault<\$%&?>location<\$%&?>based<\$%&?>on<\$%&?>AE<\$ %&?>method<\$%&?>are:<\$%&?>(i)<\$%&?>measurement<\$%&?>of<\$%&?>the<\$%&?>time<\$%&?>difference<\$%&?>of<\$%&?>arri val<\$%&?>(TDOA)<\$%&?>of<\$%&?>the<\$%&?>acoustic<\$%&?>signals,<\$%&?>(ii)<\$%&?>measurement<\$%&?>of<\$%&?>the<\$% &?>acoustic<\$%&?>signal<\$%&?>amplitude<\$%&?>in<\$%&?>different<\$%&?>areas<\$%&?>of<\$%&?>a<\$%&?>transformer<\$%&? >tank<\$%&?>(standard<\$%&?>auscultatory<\$%&?>technique,<\$%&?>SAT),<\$%&?>(iii)<\$%&?>advanced<\$%&?>auscultatory<\$% &?>technique<\$%&?>(AAT),<\$%&?>(iv)<\$%&?>estimation<\$%&?>of<\$%&?>the<\$%&?>direction<\$%&?>of<\$%&?>arrival<\$%&?>( The analysis of the results of PD source location, obtained with the triangulation method showed that both sources of discharges were placed near the symmetry axis of the phase *HV 2* bushing and the transformer tank (Fig. 10a and 10b). On the basis of the investigation results, a hypothesis was assumed that partial discharges were generated inside the insulation of the winding leads or in the support beam that is close to the transformer tank.

**Figure 11.** The number of AE events registered during daily monitoring of 160 MVA transformer

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**Figure 12.** Amplitude of AE events registered during the daily monitoring of 160 MVA transformer.

**Figure 10.** The result of the PD source location obtained with the use of triangulation method presented in the XYZ coordinates system (the XZ plane illustrates the wall of tank from the HV side) (a) and projection of calculated PD co‐ ordinates (XYZ) to the XZ plane (b).

Based on the obtained results of defect location and the analysis of the external structures of the transformer tank, the places, where acoustic emission sensors of monitoring system should be mounted, were selected (Fig. 9b). Due to the fact that AE sensors were placed close to located defect, on each of the four channels a similar number of acoustic events was recorded (Fig. 11). The amplitude of the signal recorded by each sensor was similar as well.

The same was also the average amplitude of the signal recorded by each sensor (Fig. 12). However, when looking at the distribution of number of AE events, it can be noted that daily activity profiles of the partial discharges recorded by pairs of sensor (00&01 and 02&03) were similar. This fact suggests the existence of two defects, which was already mentioned after the analysis of the location results with the use of *Advanced Auscultatory Technique* (see Fig. 9b).

**Figure 11.** The number of AE events registered during daily monitoring of 160 MVA transformer

(a) (b)

**Figure 10.** The result of the PD source location obtained with the use of triangulation method presented in the XYZ coordinates system (the XZ plane illustrates the wall of tank from the HV side) (a) and projection of calculated PD co‐

Based on the obtained results of defect location and the analysis of the external structures of the transformer tank, the places, where acoustic emission sensors of monitoring system should be mounted, were selected (Fig. 9b). Due to the fact that AE sensors were placed close to located defect, on each of the four channels a similar number of acoustic events was recorded (Fig.

The same was also the average amplitude of the signal recorded by each sensor (Fig. 12). However, when looking at the distribution of number of AE events, it can be noted that daily activity profiles of the partial discharges recorded by pairs of sensor (00&01 and 02&03) were similar. This fact suggests the existence of two defects, which was already mentioned after the analysis of the location results with the use of *Advanced Auscultatory Technique* (see Fig. 9b).

11). The amplitude of the signal recorded by each sensor was similar as well.

ordinates (XYZ) to the XZ plane (b).

106 Acoustic Emission - Research and Applications

**Figure 12.** Amplitude of AE events registered during the daily monitoring of 160 MVA transformer.

Further interesting conclusions arise when comparing both the number of events and the average amplitude of the acoustic signal with daily load of the unit (Fig. 13). One can observe that the increase in the load is associated with increase of intensity and amplitude of partial discharges. Load peaks, occurring at 21:00 and 12:00, are accompanied by the largest PD intensity and highest average amplitude of registered acoustic signals. Probably, the temper‐ ature increased closed to defect, which was a consequence of the growth in the value of the current, causing intensification of the partial discharge phenomenon. Analysis of the impact of voltage changes caused by tap changes of the autotransformer, did not show any significant correlation with respect to the recorded acoustic signal (voltage change were small indeed).

**5.2.** *Case study 2* **– Short-term monitoring (weekly) of 250 MVA transformer**

smaller amplitude than it was in the case of the low voltage side.

(AAT) on low voltage side of the 250 MVA transformer

method was performed.

A reason for installing the monitoring system to 250 MVA transformer was to observe from the beginning of 2011, the systematic increase of dissolved gases in oil (mostly hydrogen). The same year, in June, the location of the partial discharge sources by means of acoustic emission

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During the tests, several areas were located on tank in which recorded acoustic emission pulses were characterized by high amplitude. In the case of the lower voltage side (110 kV), repeatable pulses with the largest amplitude were recorded close to neutral point bushing (*N*). In addition, on the same side, sporadically occurring high amplitude PD pulses localized in phase *LV 1* and *LV 3* were recorded. During the measurements, any discharge pulses in phase *LV 2* were not registered (Fig. 14). In the case of the high voltage side (400 kV) sporadically occurring partial discharge pulses were also recorded, however, they were characterized by much

**Figure 14.** Results of PD source location (*Acoustic Emission Map*) obtained using advanced auscultatory technique

Duetofurthersystematicincreaseinthelevelofhydrogendissolvedinoilandthealarmingresults of the detection and location of partial discharges, in December 2011 the transformer owner decidedtoinstallamonitoringsystemforaperiodofoneweek.Basedontheresultsofthelocation, obtained before, three AE sensors have been mounted on the tank on the low voltage side near selected areas of greatest loudness (phase *LV 1* – sensor '02', phase *LV 3* – sensor '00', proximity to neutral point insulator – sensor '03'). The last sensor '01' was mounted in phase *LV 2* (as refer‐ ence sensor), where the test results showed that it is free from partial discharges. Such arrange‐ ment of AE sensors allowed the simultaneous monitoring of all phases of the transformer, with particular emphasis on critical points, which were fixed on the tank before. In the characteristics ofthenumberofacousticeventsregisteredduringtheweeklymonitoringofthetransformerwere

**Figure 13.** The value of daily load of the monitored transformer, registered in SYNDIS ES system

Observation of daily profile of PD activity changes also shows the advantages of on-line monitoring and imperfections of the standard approach to measuring partial discharges.

As one can observe in Fig. 11-12, the time of measurement can determine the quality of the analysis. During the day, both periods occurred in the monitored unit: extinction of partial discharges and their particular intensification.

Therefore one can conclude, that the choice of date and time for the implementation of periodic diagnostic tests by AE method (lasting usually no longer than a few hours) may have a fundamental importance for correct and reliable assessment of transformer insulation system. Of course, due to the stochastic nature of the partial discharge phenomenon, the most reliable results are obtained by monitoring the unit for a period of time at least one day.

#### **5.2.** *Case study 2* **– Short-term monitoring (weekly) of 250 MVA transformer**

Further interesting conclusions arise when comparing both the number of events and the average amplitude of the acoustic signal with daily load of the unit (Fig. 13). One can observe that the increase in the load is associated with increase of intensity and amplitude of partial discharges. Load peaks, occurring at 21:00 and 12:00, are accompanied by the largest PD intensity and highest average amplitude of registered acoustic signals. Probably, the temper‐ ature increased closed to defect, which was a consequence of the growth in the value of the current, causing intensification of the partial discharge phenomenon. Analysis of the impact of voltage changes caused by tap changes of the autotransformer, did not show any significant correlation with respect to the recorded acoustic signal (voltage change were small indeed).

**Figure 13.** The value of daily load of the monitored transformer, registered in SYNDIS ES system

discharges and their particular intensification.

108 Acoustic Emission - Research and Applications

Observation of daily profile of PD activity changes also shows the advantages of on-line monitoring and imperfections of the standard approach to measuring partial discharges.

As one can observe in Fig. 11-12, the time of measurement can determine the quality of the analysis. During the day, both periods occurred in the monitored unit: extinction of partial

Therefore one can conclude, that the choice of date and time for the implementation of periodic diagnostic tests by AE method (lasting usually no longer than a few hours) may have a fundamental importance for correct and reliable assessment of transformer insulation system. Of course, due to the stochastic nature of the partial discharge phenomenon, the most reliable

results are obtained by monitoring the unit for a period of time at least one day.

A reason for installing the monitoring system to 250 MVA transformer was to observe from the beginning of 2011, the systematic increase of dissolved gases in oil (mostly hydrogen). The same year, in June, the location of the partial discharge sources by means of acoustic emission method was performed.

During the tests, several areas were located on tank in which recorded acoustic emission pulses were characterized by high amplitude. In the case of the lower voltage side (110 kV), repeatable pulses with the largest amplitude were recorded close to neutral point bushing (*N*). In addition, on the same side, sporadically occurring high amplitude PD pulses localized in phase *LV 1* and *LV 3* were recorded. During the measurements, any discharge pulses in phase *LV 2* were not registered (Fig. 14). In the case of the high voltage side (400 kV) sporadically occurring partial discharge pulses were also recorded, however, they were characterized by much smaller amplitude than it was in the case of the low voltage side.

**Figure 14.** Results of PD source location (*Acoustic Emission Map*) obtained using advanced auscultatory technique (AAT) on low voltage side of the 250 MVA transformer

Duetofurthersystematicincreaseinthelevelofhydrogendissolvedinoilandthealarmingresults of the detection and location of partial discharges, in December 2011 the transformer owner decidedtoinstallamonitoringsystemforaperiodofoneweek.Basedontheresultsofthelocation, obtained before, three AE sensors have been mounted on the tank on the low voltage side near selected areas of greatest loudness (phase *LV 1* – sensor '02', phase *LV 3* – sensor '00', proximity to neutral point insulator – sensor '03'). The last sensor '01' was mounted in phase *LV 2* (as refer‐ ence sensor), where the test results showed that it is free from partial discharges. Such arrange‐ ment of AE sensors allowed the simultaneous monitoring of all phases of the transformer, with particular emphasis on critical points, which were fixed on the tank before. In the characteristics ofthenumberofacousticeventsregisteredduringtheweeklymonitoringofthetransformerwere summarized in figure 15. In turn, on figure 16 the values of the oil temperature in top layer and voltages of monitored transformer registered in SYNDIS ES system were presented.

Analysis of the characteristics showing the number of AE events recorded by the monitoring system confirmed the presence of partial discharges in phase *LV 1* and *LV 3* and the absence or presence of a few discharges in phase *LV 2* and close to neutral point bushing (Fig. 15). Monitoring showed that pulses with the highest intensity and energy are generated in phase C. The recorded PD pulses were unstable, and their ignition took place only in periods of voltage growth (Fig. 16). Moreover, it was noted that the moment of PD ignition correlates with temperature minima of top layer of oil, which may have a relationship with some dynamic changes in moisture at the interface of oil-paper insulation, described for example in (Borsi &

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**5.3.** *Case study 3* **─ Long-term (continuous) monitoring of 330 MVA transformer**

In this case the choice of the research object, on which continuous monitoring was tested, did not resulted from bad condition of the transformer. The primary purpose was integration of the PD monitoring system with the superior system (SYNDIS ES) and evaluation of opportu‐ nities for their cooperation. However, as in other cases, place of sensors location were selected basing on previously carried out detection and PD sources location using *Advanced Ausculta‐*

**Figure 17.** Results of PD source location (*Acoustic Emission Maps*) obtained using advanced auscultatory technique

Acoustic sensors were installed in each HV-phase and on the tank of on-load tap changer

For the moment, the system worked continuously and without failure for about 9 months. At that time, it registered several periods of partial discharge activity, but their low intensity not suggested the possibility of a serious threat. However, the ability to correlate, e.g. the moment of PD initiation with different parameters recorded by the SYNDIS ES system (oil temperature, load, voltage etc.), seems to be interesting, especially from a scientific point of view and the possibilities for development and improvement of inference rules implemented in the software

(OLTC), in the place, where the pulses with the highest amplitude were recorded.

Schroder, 1994; Buerschaper et al., 2003; Sokolov et al., 1999).

*tory Technique* (Fig. 17).

of monitoring system.

(AAT) on HV and OLTC side of the 330 MVA transformer

**Figure 15.** The number of EA events registered during the weekly monitoring of tested 250 MVA transformer

**Figure 16.** The value of the top layer of the oil temperature and voltages of the monitored 250 MVA transformer reg‐ istered in SYNDIS ES system

Analysis of the characteristics showing the number of AE events recorded by the monitoring system confirmed the presence of partial discharges in phase *LV 1* and *LV 3* and the absence or presence of a few discharges in phase *LV 2* and close to neutral point bushing (Fig. 15). Monitoring showed that pulses with the highest intensity and energy are generated in phase C. The recorded PD pulses were unstable, and their ignition took place only in periods of voltage growth (Fig. 16). Moreover, it was noted that the moment of PD ignition correlates with temperature minima of top layer of oil, which may have a relationship with some dynamic changes in moisture at the interface of oil-paper insulation, described for example in (Borsi & Schroder, 1994; Buerschaper et al., 2003; Sokolov et al., 1999).

#### **5.3.** *Case study 3* **─ Long-term (continuous) monitoring of 330 MVA transformer**

summarized in figure 15. In turn, on figure 16 the values of the oil temperature in top layer and

voltages of monitored transformer registered in SYNDIS ES system were presented.

110 Acoustic Emission - Research and Applications

**Figure 15.** The number of EA events registered during the weekly monitoring of tested 250 MVA transformer

**Figure 16.** The value of the top layer of the oil temperature and voltages of the monitored 250 MVA transformer reg‐

istered in SYNDIS ES system

In this case the choice of the research object, on which continuous monitoring was tested, did not resulted from bad condition of the transformer. The primary purpose was integration of the PD monitoring system with the superior system (SYNDIS ES) and evaluation of opportu‐ nities for their cooperation. However, as in other cases, place of sensors location were selected basing on previously carried out detection and PD sources location using *Advanced Ausculta‐ tory Technique* (Fig. 17).

**Figure 17.** Results of PD source location (*Acoustic Emission Maps*) obtained using advanced auscultatory technique (AAT) on HV and OLTC side of the 330 MVA transformer

Acoustic sensors were installed in each HV-phase and on the tank of on-load tap changer (OLTC), in the place, where the pulses with the highest amplitude were recorded.

For the moment, the system worked continuously and without failure for about 9 months. At that time, it registered several periods of partial discharge activity, but their low intensity not suggested the possibility of a serious threat. However, the ability to correlate, e.g. the moment of PD initiation with different parameters recorded by the SYNDIS ES system (oil temperature, load, voltage etc.), seems to be interesting, especially from a scientific point of view and the possibilities for development and improvement of inference rules implemented in the software of monitoring system.

For example, figure 18 shows the number of PD pulses registered by two sensors with numbers '01' and '02', installed respectively in phase *HV 1* and *HV 2*. As one can observe, partial discharges were transient, but comparison with other parameters, such as temperature and voltage (Fig. 19), allows to detect a correlation. As it was described in the previous case, the moment of PD initiation and growth of its intensity were connected not only with an increase in voltage, but at the same time, with relatively low value of oil temperature (20-30°C). Particularly high partial discharge activity has been reported in cases, in which the period of oil cooling and at the same time the growth of voltage lasted at least several hours. In the phase *HV 1* (sensor '01') this situation took place from 25 until 27 February, and in phase *HV 2* (sensor

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The chapter presents detailed description and features of the *Time Difference of Arrival* (TDOA) technique and new *Advanced Auscultatory Technique* (AAT) for location of partial discharge sources, as well as some examples of its practical application in power transformer diagnostics.

The developed by the authors *Advanced Auscultatory Technique* constitutes a synergistic combination of two diagnostic methods: (i) the acoustic emission (AE) and (ii) the conventional

The presented research results proved numerous advantages of the AAT, among which the

**•** reduction of influence of partial discharge energy fluctuations on energy of registered AE pulses, which are the main reason of the PD source location errors with the standard

**•** clear and readable presentation of the fault location results in form of a high-resolution

**•** uncomplicated and quick PD location technique, particularly useful for transformer manufacturing plants and repair companies equipped with electrically shielded HV

The partial discharge online monitoring in power transformer based on AE method was

The presented results largely confirmed the advantages offered by the partial discharge

**•** possibility of linking the partial discharge activity with other events or parameters recorded

monitoring using the acoustic emission method, of which the most important are: **•** ability to assess the profile of daily, weekly or monthly partial discharge activity,

by the service station or other systems monitoring transformer work,

**•** the possibility of correlation between AE parameters and apparent charge,

'02') between 12 and 13 day of the same month.

electrical PD detection method according to IEC 60270.

intensity graph (Acoustic Emission Map),

another important topic covered in the chapter.

**6. Conclusions**

most important are:

laboratory.

auscultatory technique,

**Figure 18.** The number of AE events registered on 330 MVA transformer, where the long-term test of partial dis‐ charge monitoring system was carried out.

**Figure 19.** The oil temperature at top layer and values of voltages of the monitored 330 MVA transformer registered in SYNDIS ES system

For example, figure 18 shows the number of PD pulses registered by two sensors with numbers '01' and '02', installed respectively in phase *HV 1* and *HV 2*. As one can observe, partial discharges were transient, but comparison with other parameters, such as temperature and voltage (Fig. 19), allows to detect a correlation. As it was described in the previous case, the moment of PD initiation and growth of its intensity were connected not only with an increase in voltage, but at the same time, with relatively low value of oil temperature (20-30°C). Particularly high partial discharge activity has been reported in cases, in which the period of oil cooling and at the same time the growth of voltage lasted at least several hours. In the phase *HV 1* (sensor '01') this situation took place from 25 until 27 February, and in phase *HV 2* (sensor '02') between 12 and 13 day of the same month.

#### **6. Conclusions**

**Figure 18.** The number of AE events registered on 330 MVA transformer, where the long-term test of partial dis‐

**Figure 19.** The oil temperature at top layer and values of voltages of the monitored 330 MVA transformer registered

charge monitoring system was carried out.

112 Acoustic Emission - Research and Applications

in SYNDIS ES system

The chapter presents detailed description and features of the *Time Difference of Arrival* (TDOA) technique and new *Advanced Auscultatory Technique* (AAT) for location of partial discharge sources, as well as some examples of its practical application in power transformer diagnostics.

The developed by the authors *Advanced Auscultatory Technique* constitutes a synergistic combination of two diagnostic methods: (i) the acoustic emission (AE) and (ii) the conventional electrical PD detection method according to IEC 60270.

The presented research results proved numerous advantages of the AAT, among which the most important are:


The partial discharge online monitoring in power transformer based on AE method was another important topic covered in the chapter.

The presented results largely confirmed the advantages offered by the partial discharge monitoring using the acoustic emission method, of which the most important are:


[9] Lundgaard, L. E. (1992). Partial discharge XIV. Acoustic partial discharge detection-

Power Transformer Diagnostics Based on Acoustic Emission Method

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**•** possibility of partial discharge sources location.

#### **Author details**

Wojciech Sikorski and Krzysztof Walczak

Poznan University of Technology, Poland

#### **References**


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**•** ability to assess the dynamics of defect development,

**•** possibility of partial discharge sources location.

measurement procedures, and

114 Acoustic Emission - Research and Applications

Wojciech Sikorski and Krzysztof Walczak

Poznan University of Technology, Poland

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116 Acoustic Emission - Research and Applications

*(ICPADM)*, , 550-552.

$$\ln(dN(\mathcal{U})/dt) = A\mathcal{U} + \mathcal{B}; \quad A = ADC \tag{1}$$





$$\vec{X}^T = \{A\_1, A\_2, \dots, A\_{\nu'}, B\_1, B\_2, \dots, B\_{\nu'}, \mathbb{C}\_1, \mathbb{C}\_2, \dots, \mathbb{C}\_{r'}, D\_1, D\_2, \dots, D\_s\} \tag{2}$$

$$
\Delta \vec{\mathcal{W}}(t+1) = \eta(t)G(t)[\vec{X} - \vec{\mathcal{W}}] \tag{3}
$$





**Figure 13.** Z3 source: a) description of AE signal, b) frequency characteristic, c) phase-time characteristic, d) averaging phase characteristic, e) and f) averaging STFT spectrograms recorded in measuring conditions: measurement by means of WD sensor placed at P3 measuring point of ,,M" bar, the supply voltage of the bar 15.5 kV, measured value of the apparent charge 3.4 nC

**Figure 14.** Z6 source: a) description of AE signal, b) frequency characteristic, c) phase-time characteristic, d) averaging phase characteristic, e) and f) averaging STFT spectrograms recorded in measuring conditions: measurement by means of WD sensor placed at P6 measuring point of ,,M" bar, the supply voltage of the bar 31 kV, measured value of

Testing of Partial Discharges and Location of Their Sources in Generator Coil Bars…

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139

the apparent charge 26 nC

**Figure 14.** Z6 source: a) description of AE signal, b) frequency characteristic, c) phase-time characteristic, d) averaging phase characteristic, e) and f) averaging STFT spectrograms recorded in measuring conditions: measurement by means of WD sensor placed at P6 measuring point of ,,M" bar, the supply voltage of the bar 31 kV, measured value of the apparent charge 26 nC

**Figure 13.** Z3 source: a) description of AE signal, b) frequency characteristic, c) phase-time characteristic, d) averaging phase characteristic, e) and f) averaging STFT spectrograms recorded in measuring conditions: measurement by means of WD sensor placed at P3 measuring point of ,,M" bar, the supply voltage of the bar 15.5 kV, measured value

of the apparent charge 3.4 nC

138 Acoustic Emission - Research and Applications

### **12. Cumulative analysis of PD testing results made by means of joint electric-acoustic methodology**

The source of the most intensive partial discharges appears within ,,D" bar near measuring point P2. This source is created by inclusions near HV electrode (inside dielectric, near surface of the bar). Ignition of partial discharges at point P2 takes place already under the supply voltage of 7 kV (about 52% UN). This source is created by inclusions near LV electrode. The most compose description of PD, given by expert diagnostic program TEAS (for the supply voltage 15 kV), corresponds with activity of Z1 source and growth of activity of Z2 source. Growth of apparent charge value is caused by properties of PD source which is located near point P2. Maximal value of ADP descriptor and maximal value of apparent charge was

**Electric method AE method**

ADP within

kHz

Testing of Partial Discharges and Location of Their Sources in Generator Coil Bars…





**Table 4.** Statement of results of PD analysis made by means of electric-acoustic methodology for ,,M" bar (UN =13.8

bands of: Location of sources [150,500]

> Z3 source – near P3 Z4 source – near P5

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141

Z3 source – near P3 Z4 source – near P5

Z3 source – near P3 Z4 source – near P5

Z3 source – near P3 Z4 source – near P5 Z5,Z6 sources – near ends

Z3 source – near P3 Z4 source – near P5 Z5,Z6 sources – near ends

[20,60] kHz

registered for the band of 20 – 60 kHz under the supply voltage of 15 kV.

Diagnosis results received from TEAS program: Kind of PD source – correctness of diagnose (estimation by probability)

10 2.2 External disturbances – 0.03 -24.0 -2.1

Superposition of inner discharges and corona

(own analysis of Authors for lack of a standard in the library of profiles)

1% surface discharge – 0.12

<sup>25</sup> <sup>70</sup> Corona appeared at many points which dominates inner discharges – 0.21

<sup>30</sup> <sup>26</sup> Corona appeared at many points which dominates inner discharges – 0.26

<sup>20</sup> 6.5 Discharge inside dielectric,

U kV

15 3.4

kV)

Q nC

Results of analyses concerning testing of PD within ,,D" and ,,M" bar, received simultaneously from electric and AE method, are stated in Tables 3 and 4. The statement contains supply voltages and the following measured or calculated quantities for signals recorded under particular supply voltages: apparent charge, ADP descriptors for AE signal which gives maximum within the suitable group of AE signals (after analysis within bands of 150 – 500 kHz and 20 – 60 kHz) and properties of PD sources determine independently by means of electric and acoustic method. There are properties: kind of PD determined by means of expert diagnostic program TEAS (electric method) and location of PD sources (AE method). Such a statement enables us to obtain more complete information on the kind and the place in which partial discharges appear.


**Table 3.** Statement of results of PD analysis made by means of electric-acoustic methodology for ,,D" bar (UN =13.8 kV)

The source of the most intensive partial discharges appears within ,,D" bar near measuring point P2. This source is created by inclusions near HV electrode (inside dielectric, near surface of the bar). Ignition of partial discharges at point P2 takes place already under the supply voltage of 7 kV (about 52% UN). This source is created by inclusions near LV electrode. The most compose description of PD, given by expert diagnostic program TEAS (for the supply voltage 15 kV), corresponds with activity of Z1 source and growth of activity of Z2 source. Growth of apparent charge value is caused by properties of PD source which is located near point P2. Maximal value of ADP descriptor and maximal value of apparent charge was registered for the band of 20 – 60 kHz under the supply voltage of 15 kV.

**12. Cumulative analysis of PD testing results made by means of joint**

Results of analyses concerning testing of PD within ,,D" and ,,M" bar, received simultaneously from electric and AE method, are stated in Tables 3 and 4. The statement contains supply voltages and the following measured or calculated quantities for signals recorded under particular supply voltages: apparent charge, ADP descriptors for AE signal which gives maximum within the suitable group of AE signals (after analysis within bands of 150 – 500 kHz and 20 – 60 kHz) and properties of PD sources determine independently by means of electric and acoustic method. There are properties: kind of PD determined by means of expert diagnostic program TEAS (electric method) and location of PD sources (AE method). Such a statement enables us to obtain more complete information on the kind and the place in which

**Electric method AE method**

ADP within

kHz






bands of: Location of sources [150,500]

> Z1 source – near P2

> Z1 source – near P2 Z2 source – near P5

> Z1 source – near P2 Z2 source – near P5

> Z1 source – near P2 Z2 source – near P5

> Z1 source – near P2 Z2 source – near P5

[20,60] kHz

Diagnosis results received from TEAS program: Kind of PD source – correctness of diagnose (estimation by probability)

Inclusions near HV electrode (inside dielectric,

Inclusions near HV electrode (inside dielectric,

Inclusions near HV electrode (inside dielectric,

Inclusions near HV electrode (inside dielectric,

Inclusions near HV electrode (inside dielectric,

**Table 3.** Statement of results of PD analysis made by means of electric-acoustic methodology for ,,D" bar (UN =13.8 kV)

Inclusions near LV electrode (in upper part

near surface of the bar) – 0.57

near surface of the bar) – 0.41

Inclusions inside dielectric – 0.15

near surface of the bar) – 0.39

near surface of the bar) – 0.44

near surface of the bar) – 0.86

of dielectric) – 0.21

**electric-acoustic methodology**

140 Acoustic Emission - Research and Applications

partial discharges appear.

U kV

10 3.5

15 3.8

20 2.2

25 2.2

30 2.0

Q nC


**Table 4.** Statement of results of PD analysis made by means of electric-acoustic methodology for ,,M" bar (UN =13.8 kV)

The source of the most intensive partial discharges within ,,M" bar appears near measuring point P3. It is caused by inner discharges. Additionally, the source within area of measuring points P5 and P6 is active for supply voltages 25 and 30 kV. There is also a source near measuring point P5 within the band of 20 – 60 kHz. Acoustic method shows weak sensitivity for many-point corona discharge given growth of apparent charge up to value of 70 nC.

increases. Values of apparent charges introduced by PD sources into ,,M" bar reached

Testing of Partial Discharges and Location of Their Sources in Generator Coil Bars…

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143

AE measurements were made at six measuring points of each tested bar. Investigation results obtained from AE method determine the following frequency bands, dominant in AE signals: 20 - 40 kHz, 70 - 110 kHz, 120 - 150 kHz, 200 - 240 kHz, 270 - 290 kHz and 450-490 kHz. ADC and ADP descriptors calculated for recorded signals order theses signals according their advanced degree. They are a base for location of AE sources by means of the original method

In result of analysis of AE signals within the band of [150,500] kHz such a location od PD sources for different values of the supply voltage have been made; six PD sources were located

Obtained results were proved by location resultant from application of Kohonen network.

AE signals recorded within the band [20,60] kHz were analyzed additionally. Analysis of AE signals in different frequency bands showed the same location of PD sources and proved

The cumulative analysis of PD test results made by means of complex electric-accoustic methodology enables us to obtain more complete information about the kind and the place of

1 Department of Optoelectronics, Silesian University of Technology, Gliwice, Poland

2 Institute of Power Systems & Control, Silesian University of Technology, Gliwice, Poland

[1] Bartnikas, P. *PDs their mechanism, detection and measurement,* IEEE Trans. on Dielec‐

[2] Lundgaard, L. E. *PD- part Xii: acoustic PD detection- fundamental considerations,*IEEE EI

[3] Lundgaard, L. E. *PD-part: Xii acoustic PD detection- practical application*IEEE EI Maga‐

suprising anomalous great value (70 nC).

of advanced degree of AE signals.

changes of AE signals during propagation.

occurrence of partial discharges.

**Author details**

Franciszek Witos1

**References**

and results of basic analysis for these sources were presented.

and Zbigniew Gacek2

\*Address all correspondence to: franciszek.witos@polsl.pl

trics and Electrical Insulation, (2002). , 9(5), 763-808.

Magazine, (1992). , 8(4), 25-31.

zine, (1992). , 8(5), 34-43.

### **13. Recapitulation**

The original method, worked out to analyze of AE signals at basic and advanced level, is presented. The basic analysis is made in domains of time, frequency and time-frequency, whereas advanced analysis describes properties of AE signals in domain of threshold.

The results of basic analysis are as follows:

–the signal after filtration with its minimal, maximal and RMS values (the band pass filter of 5. order is applied to filtration which band is given as a frequency range at the frequency characteristic),

–spectral power density as frequency characteristic of the signal with the frequency for main maximum and spectrum value for this frequency,

–phase-time characteristic,

–averaging phase characteristic,

–three-dimensional STFT spectrogram,

–three-dimensional STFT spectrogram dropped to a phase-frequency plane.

These quantities are calculated for AE signals recorded during 100 periods of the supply voltage and describes properties of AE signal for ,,averaging" period of the supply voltage. They define frequency of bands dominant within AE signals and describe random character of AE signals appearing in analyzed phenomena.

Results of advanced analysis are distributions of counting rate and power of the signal in function of discrimination threshold as well as ADC and ADP descriptors which describe quantitatively AE signals named as advanced degree of recorded AE signals. Descriptors are a base to locate PD sources by means of advanced degree of AE signals.

Investigations of partial discharges within generator coil bars, realized as simultaneous measurements made by means of electric and acoustic method. Electric method was applied to determine apparent charge, introduced by active PD sources, and kind of recorded partial discharges (by means of TEAS program). Analysis of test results is presented for two chosen bars, designed as ,,D" (bar of the coil of the generator 120 MW, UN=13.8 kV) and ,,M" (bar of the coil of the generator 200 MW, UN=15.76 kV). The both bars are interesting from the point of vue of analyze of PD sources. In the case of ,,D" bar, PD sources introduce apparent charges whose value is at the level of several nC; this value diminues when the supply voltage increases. Values of apparent charges introduced by PD sources into ,,M" bar reached suprising anomalous great value (70 nC).

AE measurements were made at six measuring points of each tested bar. Investigation results obtained from AE method determine the following frequency bands, dominant in AE signals: 20 - 40 kHz, 70 - 110 kHz, 120 - 150 kHz, 200 - 240 kHz, 270 - 290 kHz and 450-490 kHz. ADC and ADP descriptors calculated for recorded signals order theses signals according their advanced degree. They are a base for location of AE sources by means of the original method of advanced degree of AE signals.

In result of analysis of AE signals within the band of [150,500] kHz such a location od PD sources for different values of the supply voltage have been made; six PD sources were located and results of basic analysis for these sources were presented.

Obtained results were proved by location resultant from application of Kohonen network.

AE signals recorded within the band [20,60] kHz were analyzed additionally. Analysis of AE signals in different frequency bands showed the same location of PD sources and proved changes of AE signals during propagation.

The cumulative analysis of PD test results made by means of complex electric-accoustic methodology enables us to obtain more complete information about the kind and the place of occurrence of partial discharges.

#### **Author details**

The source of the most intensive partial discharges within ,,M" bar appears near measuring point P3. It is caused by inner discharges. Additionally, the source within area of measuring points P5 and P6 is active for supply voltages 25 and 30 kV. There is also a source near measuring point P5 within the band of 20 – 60 kHz. Acoustic method shows weak sensitivity for many-point corona discharge given growth of apparent charge up to value of 70 nC.

The original method, worked out to analyze of AE signals at basic and advanced level, is presented. The basic analysis is made in domains of time, frequency and time-frequency, whereas advanced analysis describes properties of AE signals in domain of threshold.

–the signal after filtration with its minimal, maximal and RMS values (the band pass filter of 5. order is applied to filtration which band is given as a frequency range at the frequency

–spectral power density as frequency characteristic of the signal with the frequency for main

These quantities are calculated for AE signals recorded during 100 periods of the supply voltage and describes properties of AE signal for ,,averaging" period of the supply voltage. They define frequency of bands dominant within AE signals and describe random character

Results of advanced analysis are distributions of counting rate and power of the signal in function of discrimination threshold as well as ADC and ADP descriptors which describe quantitatively AE signals named as advanced degree of recorded AE signals. Descriptors are

Investigations of partial discharges within generator coil bars, realized as simultaneous measurements made by means of electric and acoustic method. Electric method was applied to determine apparent charge, introduced by active PD sources, and kind of recorded partial discharges (by means of TEAS program). Analysis of test results is presented for two chosen bars, designed as ,,D" (bar of the coil of the generator 120 MW, UN=13.8 kV) and ,,M" (bar of the coil of the generator 200 MW, UN=15.76 kV). The both bars are interesting from the point of vue of analyze of PD sources. In the case of ,,D" bar, PD sources introduce apparent charges whose value is at the level of several nC; this value diminues when the supply voltage

–three-dimensional STFT spectrogram dropped to a phase-frequency plane.

a base to locate PD sources by means of advanced degree of AE signals.

**13. Recapitulation**

142 Acoustic Emission - Research and Applications

characteristic),

–phase-time characteristic,

–averaging phase characteristic,

–three-dimensional STFT spectrogram,

The results of basic analysis are as follows:

maximum and spectrum value for this frequency,

of AE signals appearing in analyzed phenomena.

Franciszek Witos1 and Zbigniew Gacek2

\*Address all correspondence to: franciszek.witos@polsl.pl

1 Department of Optoelectronics, Silesian University of Technology, Gliwice, Poland

2 Institute of Power Systems & Control, Silesian University of Technology, Gliwice, Poland

#### **References**


[4] Witos, F, Gacek, Z, & Opilski, A. The new AE descriptor for modeled sources of PDs, Archives of Acoustic, (2002). , 27(1), 65-77.

[19] Hertz, J, Krogh, A, & Palmer, R. G. *Introduction to the theory of neural computation,*

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145

[20] Zurada, J, Barski, M, & Jedruch, W. *Artificial neural networks,* PWN, Warsaw, Poland

[21] Ossowski, S. *Neural networks for information processing,* Of. Ed. Pol. Warsaw, Warsaw,

WNT, Warsaw, Poland (1995). in Polish).

(1996). in Polish).

Poland (2000). in Polish).


[19] Hertz, J, Krogh, A, & Palmer, R. G. *Introduction to the theory of neural computation,* WNT, Warsaw, Poland (1995). in Polish).

[4] Witos, F, Gacek, Z, & Opilski, A. The new AE descriptor for modeled sources of PDs,

[5] Witos, F, & Gacek, Z. *In search of AE descriptors correlated with apparent electric charge*, ISH- XIIIth Int Symposium on High Voltage Engineering, Netherlands (2003). Smit

[6] Boczar, T, Borucki, S, Cichon, A, & Zmarzly, D. Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emis‐ sion method, IEEE Trans. on Dielectrics and Electrical Insulation, (2009). , 16(3),

[7] Witos, F, & Gacek, Z. Application of the calibrated AE to investigate properties of AE signals coming from PD sources modeled in laminar systems, Journal de Physique

[8] Witos, F, & Gacek, Z. *Application of the joint electro-acoustic method for PD investigation*

[9] LabVIEWTM and LabWindowsTM/CVITM*Signal Proces. Toolset User Manual*, National

[10] Dabrowski, M. Construction of electrical machines, WNT, Warsaw, Poland (1997). in

[11] Zondervan, J. P, Gulski, E, & Smit, J. J. *Fundamental aspects of PD pattern of on-line measurements of turbogenerators,* IEEE Trans. on Dielectric and EI, (2000). , 7(1), 59-70.

[12] Witos, F, & Gacek, Z. *Investigations of PDs in generator coil bars by means of AE: acoustic*

[13] Kaneko, T. et all: *Characterization of on-line PD in stator winding on starting hydrogenera‐ tor using AE detection method,*ISH- XIIIth Int Symposium on High Voltage Engineer‐

[14] Witos, F, Gacek, Z, & Opilski, Z. *Testing of Partial Discharges in Generator Coil Bars with the Help of Calibrated Acoustic Emission Method,*Acta Physica Polonica A, (2008). ,

[15] Kaneko, T. et all: Characteristics of on-line and off-line partial discharge on hydro‐ generator stator windings using acoustic emission detection techniques,, (2005). Pro‐ ceedings of 2005 International Symposium on Electrical Insulating Materials (ISEIM

[18] Tadeusiewicz, R. *Neural Networks*, Academic Of. Ed. RM, Warsaw, Poland (1993). in

*images and location,* CIGRE 39th Int. Session, Paris (2002). (11-101), 11-101.

ing, Netherlands (2003). Smit (ed.) 2003 Milpress, Rotterdam.

[16] Partial Discharge Detector Type TE 571Operating Manual, (2005). , 2

[17] Physical Acoustics Corporation: . *www.pacndt.com*.

*within a power transformer,* European Physical Journal ST, (2008). , 154

Archives of Acoustic, (2002). , 27(1), 65-77.

(ed.) 2003 Milpress, Rotterdam

IV, (2005). , 129, 173-177.

Instruments, (2005).

114(6-A), 249-258.

2005),5-9 June 2005

Polish).

214-223.

144 Acoustic Emission - Research and Applications

Polish).


**Chapter 7**

**Acoustic Emission in Drying Materials**

Stefan Jan Kowalski, Jacek Banaszak and

Additional information is available at the end of the chapter

Drying of wet materials is one of the oldest and most common unit operation found in di‐ verse processes such as those used in the agricultural, ceramic, chemical, food, pharmaceuti‐ cal, pulp and paper, mineral, polymer, and textile industries. It is also one of the most complex and least understood operations because of the difficulties and deficiencies in mathematical descriptions of the phenomena of simultaneous – and often coupled and mul‐ tiphase – transport of heat, mass, and momentum in saturated porous materials. Drying is therefore an amalgam of science, technology, and art, or know-how based on extensive ex‐ perimental observations and operating experience [Strumiłło, 1983; Mujumdar (Ed.), 2007].

Drying processes ought to be appropriately arranged and operated to obtain a high quality dried products, that is, products without excessive deformations, surface cracks, and above all crosswise fractures. A non-uniform moisture distribution in products arising during dry‐ ing causes a non-uniform material shrinkage and generates stresses, which are responsible for permanent deformations and material fracture. A risk of fracture in drying samples is possible to analyze both theoretically and experimentally. Mechanistic drying models make the basis for numerical simulations of drying kinetics and analysis of the drying induced stresses [Kowalski, 2003]. In this way it is possible to determine the spots, where the drying induced stresses reach maximum and the possibly crack may occur [Kowalski and Rybicki, 2007]. The theoretical predictions are confronted with the experimental data obtained due to application of the acoustic emission method (AE), which enables monitoring *on line* the de‐

velopment of the drying induced fractures caused by stresses [Kowalski et al., 2000].

The acoustic emission method (AE) is a non-destructive method allowing indirect control of micro- and macro-fracture development during drying and above all the identification of the

> © 2013 Kowalski et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Kowalski et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Kinga Rajewska

**1. Introduction**

http://dx.doi.org/10.5772/54796

### **Chapter 7**

## **Acoustic Emission in Drying Materials**

Stefan Jan Kowalski, Jacek Banaszak and Kinga Rajewska

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/54796

#### **1. Introduction**

Drying of wet materials is one of the oldest and most common unit operation found in di‐ verse processes such as those used in the agricultural, ceramic, chemical, food, pharmaceuti‐ cal, pulp and paper, mineral, polymer, and textile industries. It is also one of the most complex and least understood operations because of the difficulties and deficiencies in mathematical descriptions of the phenomena of simultaneous – and often coupled and mul‐ tiphase – transport of heat, mass, and momentum in saturated porous materials. Drying is therefore an amalgam of science, technology, and art, or know-how based on extensive ex‐ perimental observations and operating experience [Strumiłło, 1983; Mujumdar (Ed.), 2007].

Drying processes ought to be appropriately arranged and operated to obtain a high quality dried products, that is, products without excessive deformations, surface cracks, and above all crosswise fractures. A non-uniform moisture distribution in products arising during dry‐ ing causes a non-uniform material shrinkage and generates stresses, which are responsible for permanent deformations and material fracture. A risk of fracture in drying samples is possible to analyze both theoretically and experimentally. Mechanistic drying models make the basis for numerical simulations of drying kinetics and analysis of the drying induced stresses [Kowalski, 2003]. In this way it is possible to determine the spots, where the drying induced stresses reach maximum and the possibly crack may occur [Kowalski and Rybicki, 2007]. The theoretical predictions are confronted with the experimental data obtained due to application of the acoustic emission method (AE), which enables monitoring *on line* the de‐ velopment of the drying induced fractures caused by stresses [Kowalski et al., 2000].

The acoustic emission method (AE) is a non-destructive method allowing indirect control of micro- and macro-fracture development during drying and above all the identification of the

© 2013 Kowalski et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Kowalski et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

period and also the place where the fractures start to develop. In this sense the AE is a method that enables control of drying process and help to protect the material against destruction [Kowalski, 2010]. Thus, the EA provides a unique advantage of early detection of subcritical crack growth and recognize when and where the crack is growing. The cracks and deforma‐ tions arising inside dried materials constitute the AE source. The intensity of AE signals, their number and energy inform about the state and magnitude of stresses [Kowalski, (2002), Kowalski et al., (2004)].

**•** Based on the authors' experience and the performed up to now experiments, it was stated that AE descriptors best reflecting the character of mechanical phenomena occurring in

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 149

**•** *Hits rate.* This descriptor shows the dynamics of the destruction development (e.g. a rise of temperature drying involves rapid growth of the AE hits rate). Moreover, this descriptor indicates the stages of drying, in which the reduction or increase of the AE activity takes

**•** *The hit of maximum energy*. This descriptor is more useful than that "energy of hits" as it shows the single hit with maximum energy in a given time interval. The descriptor "energy

**•** *Crest value*. This descriptor presents the intensity of hits in time. It is a very significant

**•** *The total number of hits and the total energy of hits*. These parameters show some individual phenomena occurring during drying. Thanks to these descriptors it is possible to distinguish stages of drying in which some irregular changes of the AE energy or the AE hits rate appear. These descriptors point out the critical moments of drying, in which the fracture of drying

The registered by the equipment AE signals are characterized by two fundamental param‐ eters: the amplitude and the time of signal duration. The relative energy of each signal, called also the acoustic energy of AE signal, is possible to determine integrating the sur‐ face under the envelope curve of this signal. It is a relevant characteristics of the magni‐ tude and power of the AE signal source. By application of the AE method to monitoring of drying processes, which are characterized with constant reduction of moisture content (MC) in dried materials, it should be taken into account that the AE energy depends on the material MC. If assume that the sources of AE signals cause cracks of a similar size, however, occurred first in wet material and next in dry one, then, the registered by the equipment AE signals will be different for these two events. This follows from damping of the acoustic waves propagating though a not perfectly elastic material. It is obvious that a more saturated material characterizes with stronger damping properties than an unsaturated one. Therefore, it is essential to take into account the damping effects by

It is necessary then to carry out the calibration of the AE energy for each examined material in dependence on its MC. There is a number of calibration methods (Malecki and Ranachowski 1994, Banaszak and Kowalski 2010). Here, the mechanical method is presented, that is, the method of falling ball (Berlinsky at al. 1990, Luong Phong 1994). The calibration was carried

of hits" presents the energy of all hits in a given time interval.

parameter illustrating the "power" of existing hits.

drying materials, are:

material may occur.

**2.2. Calibration of AE energy**

analysis of the AE energy descriptors.

out with the use of the equipment presented in figure 1.

place.

The aim of this chapter is to show the possibly using the AE method to diagnostic purposes of destruction due to monitoring materials subjected to drying. The results of the tests obtained from convective and microwave drying of ceramic and wood materials carried out in the laboratory drier equipped with the acoustic emission set-up constitute the illustrative material of this chapter.

The example under analysis concern cylindrical samples made of kaolin and wood. Based on the mechanistic drying model, the stress distribution in the samples and its evolution in time were determined. In this way the moment at which the stresses reach the critical value causing material damage was appointed [Kowalski et al. 2012]. The system of double coupled differ‐ ential equations of this model, adopted to the cylindrical geometry, was solved numerically with the help of the finite element (FEM) and the finite difference (FDM) methods. Due to AE method, the number and the energy of AE hits were measured, and the crest value of acoustic waves was appointed, and these data enabled validation of the theoretical predictions. A good adherence of the theoretical and experimental results serves for identification of fractures occurring in materials during drying.

#### **2. The essence of acoustic emission (AE) in drying**

#### **2.1. AE descriptors**


#### **2.2. Calibration of AE energy**

period and also the place where the fractures start to develop. In this sense the AE is a method that enables control of drying process and help to protect the material against destruction [Kowalski, 2010]. Thus, the EA provides a unique advantage of early detection of subcritical crack growth and recognize when and where the crack is growing. The cracks and deforma‐ tions arising inside dried materials constitute the AE source. The intensity of AE signals, their number and energy inform about the state and magnitude of stresses [Kowalski, (2002),

The aim of this chapter is to show the possibly using the AE method to diagnostic purposes of destruction due to monitoring materials subjected to drying. The results of the tests obtained from convective and microwave drying of ceramic and wood materials carried out in the laboratory drier equipped with the acoustic emission set-up constitute the illustrative material

The example under analysis concern cylindrical samples made of kaolin and wood. Based on the mechanistic drying model, the stress distribution in the samples and its evolution in time were determined. In this way the moment at which the stresses reach the critical value causing material damage was appointed [Kowalski et al. 2012]. The system of double coupled differ‐ ential equations of this model, adopted to the cylindrical geometry, was solved numerically with the help of the finite element (FEM) and the finite difference (FDM) methods. Due to AE method, the number and the energy of AE hits were measured, and the crest value of acoustic waves was appointed, and these data enabled validation of the theoretical predictions. A good adherence of the theoretical and experimental results serves for identification of fractures

**•** Different regimes of compresional acoustic waves propagating through the material from the crack places to the AE detectors attached to the samples, can be identified through the proper choice of the AE descriptors. The descriptors suitable to assessment of mechanical phenomena occuring in drying materials are selected mostly to be: *the number of acoustic emission hits*, *hit rate* (showing the dynamic of the process), *the maximum energy of hits*, and

**•** When applying the AE method to drying processes, a suitable selection of AE descriptors that let to obtain the most useful information about the phenomena occurring in dried materials is an important issue. The parameters characterizing the AE signals that are recorded by the detector inform about intensity and possible size of destruction, and therefore are significant for the precise assessment of the AE occurrence. So, the appointing of the descriptors which qualitatively fit best for description of the AE occurrence is a

Kowalski et al., (2004)].

148 Acoustic Emission - Research and Applications

occurring in materials during drying.

responsible and difficult task.

**2. The essence of acoustic emission (AE) in drying**

*the crest value* (showing the power of AE signals).

of this chapter.

**2.1. AE descriptors**

The registered by the equipment AE signals are characterized by two fundamental param‐ eters: the amplitude and the time of signal duration. The relative energy of each signal, called also the acoustic energy of AE signal, is possible to determine integrating the sur‐ face under the envelope curve of this signal. It is a relevant characteristics of the magni‐ tude and power of the AE signal source. By application of the AE method to monitoring of drying processes, which are characterized with constant reduction of moisture content (MC) in dried materials, it should be taken into account that the AE energy depends on the material MC. If assume that the sources of AE signals cause cracks of a similar size, however, occurred first in wet material and next in dry one, then, the registered by the equipment AE signals will be different for these two events. This follows from damping of the acoustic waves propagating though a not perfectly elastic material. It is obvious that a more saturated material characterizes with stronger damping properties than an unsaturated one. Therefore, it is essential to take into account the damping effects by analysis of the AE energy descriptors.

It is necessary then to carry out the calibration of the AE energy for each examined material in dependence on its MC. There is a number of calibration methods (Malecki and Ranachowski 1994, Banaszak and Kowalski 2010). Here, the mechanical method is presented, that is, the method of falling ball (Berlinsky at al. 1990, Luong Phong 1994). The calibration was carried out with the use of the equipment presented in figure 1.

Damping of the AE signal energy depends strictly on the material moisture content. Kaolin material becomes plastic for the moisture content over 27-29% and thus it stronger attenuate the AE signals than that unsaturated one. For the walnut wood the attenuation of acoustic waves becomes very strong for the moisture contents above the fiber saturation point (FSP) (ca. 30%), and there remain on a constant level. Dry wood is a very acoustic material and the energy of AE signal in such a material is weakly attenuated. Alongside with the increase of the MC up to the fiber saturation point the damping of EA waves increases radically. Over this critical MC wood stops to be acoustic material and utters characteristic hollow sounds. It should be noted a very intensive linear drop of energy in the range below the fiber saturation point, what means that wood is a material very sensitive to changeability of the moisture content, for example, music instruments made of wood should be always adjusted to the actual

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 151

There is a necessity of suitable correction of the energetic values of AE signals received from the measurement set-up, dependent on the actual MC of the material. For this purpose it is necessary to construct the calibration curve of AE energy for the tested material. It can be constructed by the best adjustment of the theoretical curve expressed by the fourth order

Figure 3 presents the results of measurements of the mean energy of AE events for kaolin and walnut wood during drying with and without taking into account the calibration curve.

(a) (b)

**Figure 3.** Comparison of mean energy of a AE hits as the function of material MC with and without taking into ac‐

The curve of calibrated AE energy for kaolin has not a significant impact on the final results by the analysis of the AE events. The character of plots with and without taking into account the calibration curve is similar. It follows from insignificant difference in AE signal attenuation for saturated and unsaturated materials. For walnut wood, however, at the beginning of drying (ca. 70% MC) there were registered low energetic AE signals only. High energetic signals appear else by about 20% MC. Taking into consideration the fact that wood strongly attenuates the AE signal with increase MC, then, it can be noticed (Fig. 3b) that the real annotated AE energy, measured with using calibration curve, is significantly higher. It influences then the analysis of AE signals for MC above the fiber saturation point (30%). Some signals arriving to

air humidity.

polynomial to the experimental data. (Fig. 2).

count of the damping effects during drying: a) kaolin, b) walnut wood

**Figure 1.** AE energy calibration set-up: a)1 – the ball's releasing mechanism and AE sensor, 2 – AC power supplier, 3 – oscilloscope, 4 –preamplifier, 5 – AE acquisition system AMSY5, 6 – computer, b) impact of dropping ball at the upper kaolin sample surface

A still ball of mass *m* = 5.60 g and diameter *d* = 11 mm was used as a source of acoustic signals having known and constant energy. The ball was planted in the grip with release mechanism, being the electromagnet connected to the power supply adaptor of direct current (2). The ball was situated at height of 10 cm above the upper surface of the sample. The AE sensor (1) was attached to the bottom surface of the sample. The registered AE signals were conveyed through the preamplifier (4) to the module unit AMS-5, where they were processed by means of control unit (6). The digital oscilloscope (3) was connected to the system to analyze the course of signal appearance and to assign the level of noise.

The release from the grip ball hit the upper surface of the sample (Fig. 1b) and generated elastic wave, which experienced damping when propagating through the not perfectly elastic sample. After its arriving to the AE sensor, it became converted into the electric signal and next undergone a suitable energetic analysis. Each test was repeated five times for each sample of given MC, and the average value was taken for further considerations. On the basis of those tests the curve of attenuation of the AE energy was determined as a function of the material MC. Figure 2 presents the results of the research carried out for the kaolin and the walnut wood.

**Figure 2.** Damping of EA energy in dependence of material MC determined in the test of falling ball: a) kaolin, b) wal‐ nut wood

Damping of the AE signal energy depends strictly on the material moisture content. Kaolin material becomes plastic for the moisture content over 27-29% and thus it stronger attenuate the AE signals than that unsaturated one. For the walnut wood the attenuation of acoustic waves becomes very strong for the moisture contents above the fiber saturation point (FSP) (ca. 30%), and there remain on a constant level. Dry wood is a very acoustic material and the energy of AE signal in such a material is weakly attenuated. Alongside with the increase of the MC up to the fiber saturation point the damping of EA waves increases radically. Over this critical MC wood stops to be acoustic material and utters characteristic hollow sounds. It should be noted a very intensive linear drop of energy in the range below the fiber saturation point, what means that wood is a material very sensitive to changeability of the moisture content, for example, music instruments made of wood should be always adjusted to the actual air humidity.

**Figure 1.** AE energy calibration set-up: a)1 – the ball's releasing mechanism and AE sensor, 2 – AC power supplier, 3 – oscilloscope, 4 –preamplifier, 5 – AE acquisition system AMSY5, 6 – computer, b) impact of dropping ball at the upper

A still ball of mass *m* = 5.60 g and diameter *d* = 11 mm was used as a source of acoustic signals having known and constant energy. The ball was planted in the grip with release mechanism, being the electromagnet connected to the power supply adaptor of direct current (2). The ball was situated at height of 10 cm above the upper surface of the sample. The AE sensor (1) was attached to the bottom surface of the sample. The registered AE signals were conveyed through the preamplifier (4) to the module unit AMS-5, where they were processed by means of control unit (6). The digital oscilloscope (3) was connected to the system to analyze the course of signal

The release from the grip ball hit the upper surface of the sample (Fig. 1b) and generated elastic wave, which experienced damping when propagating through the not perfectly elastic sample. After its arriving to the AE sensor, it became converted into the electric signal and next undergone a suitable energetic analysis. Each test was repeated five times for each sample of given MC, and the average value was taken for further considerations. On the basis of those tests the curve of attenuation of the AE energy was determined as a function of the material MC. Figure 2 presents the results of the research carried out for the kaolin and the walnut

(a) (b)

**Figure 2.** Damping of EA energy in dependence of material MC determined in the test of falling ball: a) kaolin, b) wal‐

kaolin sample surface

150 Acoustic Emission - Research and Applications

wood.

nut wood

appearance and to assign the level of noise.

There is a necessity of suitable correction of the energetic values of AE signals received from the measurement set-up, dependent on the actual MC of the material. For this purpose it is necessary to construct the calibration curve of AE energy for the tested material. It can be constructed by the best adjustment of the theoretical curve expressed by the fourth order polynomial to the experimental data. (Fig. 2).

Figure 3 presents the results of measurements of the mean energy of AE events for kaolin and walnut wood during drying with and without taking into account the calibration curve.

**Figure 3.** Comparison of mean energy of a AE hits as the function of material MC with and without taking into ac‐ count of the damping effects during drying: a) kaolin, b) walnut wood

The curve of calibrated AE energy for kaolin has not a significant impact on the final results by the analysis of the AE events. The character of plots with and without taking into account the calibration curve is similar. It follows from insignificant difference in AE signal attenuation for saturated and unsaturated materials. For walnut wood, however, at the beginning of drying (ca. 70% MC) there were registered low energetic AE signals only. High energetic signals appear else by about 20% MC. Taking into consideration the fact that wood strongly attenuates the AE signal with increase MC, then, it can be noticed (Fig. 3b) that the real annotated AE energy, measured with using calibration curve, is significantly higher. It influences then the analysis of AE signals for MC above the fiber saturation point (30%). Some signals arriving to the AE sensor are high energetic ones even for MC 70% to 60%. The initial high energetic signals for such a high MC originate from sample heating. For example, the AE signal registered for 39% MC originates from a crack in the sample. This AE event could be identified else after taking into account in the analysis the calibration curve presenting the effects of AE energy damping. The other high energetic signals, connected with the successive cracks in the sample, were registered for the sample with MC below the fiber saturation point. They are clearly visible both for the calibrated and not calibrated energy with respect to the material MC.

Figure 5 presents the scheme of the dryer. The cylindrical kaolin or wood samples were placed in the drier chamber on a special ceramic thimble with mandrel embedded on the balance located beyond the chamber. In this way the measurement of the sample weight was possible continuously during all kinds of drying, also during microwave drying. The AE detector was attached either directly to the sample foundation by convective drying or indirectly to the

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 153

The hybrid drier enabled different combination of the three methods of drying: the convective, microwave, and infrared. The drier instrumentation enable programming and control of the velocity and temperature of the air supplied to the drier chamber, control of the microwave power, two-step control of the infrared heater, and the measurement of the sample surface

ceramic thimble with mandrel by microwave drying.

**Figure 5.** Scheme of the hybrid dryer

temperature with the help of the optical pyrometer

The presented above results show how important is taking into account the damping of AE signals in studies of drying processes in which the AE method is used for monitoring of the mechanical effects, particularly in wood. Without such an approach a part of the AE signals can be wrongly interpreted, for example, some crack occurrence could not be noticed.

### **3. Experimental setup equipped with AE measurement instruments**

#### **3.1. Scheme of the equipment**

The drying tests were realized in the laboratory hybrid drier in the Department of Process Engineering, Institute of Technology and Chemical Engineering, Poznań University of Technology. Presented in this chapter experimental results has been taken for over a decade and according to technical progress our measurement instruments obviously has changed few times during those years. Figure 4 presents the photograph of the latest dryer version equipped with the acoustic emission (AE) system.

**Figure 4.** Photo of the laboratory hybrid dryer

Figure 5 presents the scheme of the dryer. The cylindrical kaolin or wood samples were placed in the drier chamber on a special ceramic thimble with mandrel embedded on the balance located beyond the chamber. In this way the measurement of the sample weight was possible continuously during all kinds of drying, also during microwave drying. The AE detector was attached either directly to the sample foundation by convective drying or indirectly to the ceramic thimble with mandrel by microwave drying.

**Figure 5.** Scheme of the hybrid dryer

the AE sensor are high energetic ones even for MC 70% to 60%. The initial high energetic signals for such a high MC originate from sample heating. For example, the AE signal registered for 39% MC originates from a crack in the sample. This AE event could be identified else after taking into account in the analysis the calibration curve presenting the effects of AE energy damping. The other high energetic signals, connected with the successive cracks in the sample, were registered for the sample with MC below the fiber saturation point. They are clearly visible both for the calibrated and not calibrated energy with respect to the material MC.

The presented above results show how important is taking into account the damping of AE signals in studies of drying processes in which the AE method is used for monitoring of the mechanical effects, particularly in wood. Without such an approach a part of the AE signals can be wrongly interpreted, for example, some crack occurrence could not be noticed.

**3. Experimental setup equipped with AE measurement instruments**

The drying tests were realized in the laboratory hybrid drier in the Department of Process Engineering, Institute of Technology and Chemical Engineering, Poznań University of Technology. Presented in this chapter experimental results has been taken for over a decade and according to technical progress our measurement instruments obviously has changed few times during those years. Figure 4 presents the photograph of the latest dryer version equipped

**3.1. Scheme of the equipment**

152 Acoustic Emission - Research and Applications

with the acoustic emission (AE) system.

**Figure 4.** Photo of the laboratory hybrid dryer

The hybrid drier enabled different combination of the three methods of drying: the convective, microwave, and infrared. The drier instrumentation enable programming and control of the velocity and temperature of the air supplied to the drier chamber, control of the microwave power, two-step control of the infrared heater, and the measurement of the sample surface temperature with the help of the optical pyrometer

#### **3.2. Methodology of AE measurement**

The AMSY-5 AE system manufactured by Vallen Systeme, Gmbh is shown in figure 4. The sample subjected to convective drying was placed on the aluminum plate with fixed piezo‐ electric sensor. Acoustic signals generated in the drying sample are registered by broad-band transducer. Next the signals were send via insulated cable to AEP3 preamplifier unit. Pream‐ plifiers was located close to AE sensors. The main task of preamplifier was amplifying and strength the signals enough that they could be sent to the distant main measurement unit. The AEP3 unit was equipped with 5 kHz to 1000 kHz filters. The main unit used in the tests had an M6 master unit for up to six AE channels from which three were fully equipped with ASSIP card. This high speed system could store up to 30 000 AE signals per second. The frequency filter inside the unit was used to eliminate noise sources. It can be set up for each channel separately. In our tests the low and high filters in the range from 12 to 850 kHz were applied to collect AE signals from kaolin and wood samples subjected to drying. The filtered AE signals were digitalized in A/D converter and stored in computer memory. The companion notebook PC had a software to control the whole measure system. During the tests the measured data were analyzed online and displayed on computer monitor so that it was possible to recognize the development of defects within the tested object.

The kaolin-clay was delivered in a dry state, and before experiments it was grinded and wetted with a predetermined amount of water and mixed to achieve a greasy paste of initial moisture content (MC) approximately equal to 0.45 [kg water/kg dry kaolin]. The greasy paste was stored and homogenized in a closed box for 48 hours to unify moisture distribu‐ tion in the whole material. The obtained in such a way soft kaolin-clay mass was used to mold cylindrical samples of 6 cm in diameter and 6 cm height. The cylindrical samples were extruded from a special instrument to preserve their regular shape (Fig. 7a), and samples of

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 155

**Figure 7.** Image of the samples used in drying tests: a) kaolin cylindrical sample with attached AE detector, b) pine

Samples of the pine wood in the form of cuboid of dimensions about 4×4×2 cm were cut from the blade of the trunk of diameter about 30 cm and keep the symmetry with regard to the axis of the core. The walnut samples were of cylindrical form with height about 26 ÷ 27 mm and the diameter about 44 ± 2 mm. These samples were cut out from walnut branches deprived of defects in the structure. They contained 5 annual growth rings on average. The samples were placed on the aluminum support to which they were pressed with the springs to get a better contact with the AE sensor. The initial humidity of pine wood samples was about 117% and walnut samples about 85%. The samples prepared in this way were used to

One of the goals of the realized tests was to interpret the AE signals that may occur during drying of kaolin-clay (Fig. 8). The first (I) characteristic group of AE signals appears at the

convective and microwave drying tests in the laboratory dryer presented above.

Figure 7 shows the shape of kaolin and pine wood samples applied in the studies.

such a form were used for drying tests.

wood sample with spring pressed the sample to the AE detector

**5. Examples of AE in drying materials**

**5.1. Convective drying of ceramic-like materials**

#### **4. Materials and conditions for AE appearance**

KOC kaolin-clay from the Surmin-Kaolin SA Company, Nowogrodziec, Poland was the material investigated experimentally and theoretically in the drying tests. For this material some charakteristic data necessary for numerical calculation of drying kinetics and drying induced stresses were already given by the Surmin-Kaolin SA Company (see Table 1 in Kowalski et al. 2000). The KOC kaolin-clay is widely applied in ceramic industry for manu‐ facturing sanitaryware and tableware. It provides a good strength and plasticity during shaping of the mentioned products and reveals a reduced amount of pyroplastic deformation in the process of their firing.

**Figure 6.** Image of kaolin KOC

The kaolin-clay was delivered in a dry state, and before experiments it was grinded and wetted with a predetermined amount of water and mixed to achieve a greasy paste of initial moisture content (MC) approximately equal to 0.45 [kg water/kg dry kaolin]. The greasy paste was stored and homogenized in a closed box for 48 hours to unify moisture distribu‐ tion in the whole material. The obtained in such a way soft kaolin-clay mass was used to mold cylindrical samples of 6 cm in diameter and 6 cm height. The cylindrical samples were extruded from a special instrument to preserve their regular shape (Fig. 7a), and samples of such a form were used for drying tests.

Figure 7 shows the shape of kaolin and pine wood samples applied in the studies.

**3.2. Methodology of AE measurement**

154 Acoustic Emission - Research and Applications

the development of defects within the tested object.

in the process of their firing.

**Figure 6.** Image of kaolin KOC

**4. Materials and conditions for AE appearance**

SEM image of kaolin KOC kaolin stack kaolin plates

The AMSY-5 AE system manufactured by Vallen Systeme, Gmbh is shown in figure 4. The sample subjected to convective drying was placed on the aluminum plate with fixed piezo‐ electric sensor. Acoustic signals generated in the drying sample are registered by broad-band transducer. Next the signals were send via insulated cable to AEP3 preamplifier unit. Pream‐ plifiers was located close to AE sensors. The main task of preamplifier was amplifying and strength the signals enough that they could be sent to the distant main measurement unit. The AEP3 unit was equipped with 5 kHz to 1000 kHz filters. The main unit used in the tests had an M6 master unit for up to six AE channels from which three were fully equipped with ASSIP card. This high speed system could store up to 30 000 AE signals per second. The frequency filter inside the unit was used to eliminate noise sources. It can be set up for each channel separately. In our tests the low and high filters in the range from 12 to 850 kHz were applied to collect AE signals from kaolin and wood samples subjected to drying. The filtered AE signals were digitalized in A/D converter and stored in computer memory. The companion notebook PC had a software to control the whole measure system. During the tests the measured data were analyzed online and displayed on computer monitor so that it was possible to recognize

KOC kaolin-clay from the Surmin-Kaolin SA Company, Nowogrodziec, Poland was the material investigated experimentally and theoretically in the drying tests. For this material some charakteristic data necessary for numerical calculation of drying kinetics and drying induced stresses were already given by the Surmin-Kaolin SA Company (see Table 1 in Kowalski et al. 2000). The KOC kaolin-clay is widely applied in ceramic industry for manu‐ facturing sanitaryware and tableware. It provides a good strength and plasticity during shaping of the mentioned products and reveals a reduced amount of pyroplastic deformation

micro structure macro structure

**Figure 7.** Image of the samples used in drying tests: a) kaolin cylindrical sample with attached AE detector, b) pine wood sample with spring pressed the sample to the AE detector

Samples of the pine wood in the form of cuboid of dimensions about 4×4×2 cm were cut from the blade of the trunk of diameter about 30 cm and keep the symmetry with regard to the axis of the core. The walnut samples were of cylindrical form with height about 26 ÷ 27 mm and the diameter about 44 ± 2 mm. These samples were cut out from walnut branches deprived of defects in the structure. They contained 5 annual growth rings on average. The samples were placed on the aluminum support to which they were pressed with the springs to get a better contact with the AE sensor. The initial humidity of pine wood samples was about 117% and walnut samples about 85%. The samples prepared in this way were used to convective and microwave drying tests in the laboratory dryer presented above.

#### **5. Examples of AE in drying materials**

#### **5.1. Convective drying of ceramic-like materials**

One of the goals of the realized tests was to interpret the AE signals that may occur during drying of kaolin-clay (Fig. 8). The first (I) characteristic group of AE signals appears at the beginning of drying process, the second (II) one in the period when the surface layer intensively shrinks, and third (III) one is noticeable sometimes in the final stage of drying and identified as being generated by the reversed stresses.

Fig. 9 presents the rate of AE hits for the five different temperatures of drying. It is seen that for the conditions of high drying rates created by high temperatures, the rate of AE hits achieve higher values than for lower temperatures. That high active emission of AE signals has a

reflection in the drying induced stresses.

**Figure 9.** The rate of AE hits during drying at different temperatures

Note that the highest peak of the AE hits, which correspond to temperature 120o

earlier than the lower peaks corresponding to the lower drying temperatures. The primary peak of AE hits appeared in 50 min of drying time, that is, when the tensional stresses at the cylinder surface reached maximum. The secondary peak is visible about 110 min drying time

shrink but the surface layer is not able to deform itself because it is almost dry. So, in these circumstances the tensional stresses arise in a core. The secondary maximum is, of course,

Another AE descriptor termed *the total energy* is useful in analysis of fracture phenomenon by drying. It measures successively the total energy released during the whole process of drying.

The curve of summarized in time acoustic energy released during drying can deliver essential information on fracture dimension occurring in the investigated body. A highly fractured material is qualified as a bad quality one. The descriptor of total energy released may serve as an indicator, whether the dried product is of good or bad quality at the end-state. Strong cracks of body structure release high energetic EA signals. The high or medium energetic signals are evidenced in figure 10 as the strait upright lines. In some cases, the high energetic signals denote macro-cracks or splits that are visible on the sample surface. Taking into considerations the above presented curve of total energy one can state that it represents a dried product of

The strength of material with damaged structure is impaired. Often a number of internal small micro-cracks arising during drying may nucleate and create macro-cracks during utilization

Figure 10 presents the rate of AE hits and the curve of total energy versus time.

C. In this time the core of the body starts to dry. The wet core wants to

C, appears

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 157

AE hits rate [N/30s]

for temperature 120o

bad quality.

much lower than the first one.

**Figure 8.** AE signals and the curve of drying

As the drying body is almost fully saturated at the initial stage of drying, the number and the maximum value of AE signals in the heating period (I) is proportional to temperature of the drying process. At this stage of drying, the thermal stresses dominate in the clay sample which are rather not so much meaningful. Unfortunately, at the initial stage of drying some of AE signals come from heating aluminium probe and the AE sensor. It is hard to decide which AE signals in this stage of drying are from the investigated kaolin-clay sample and which from other sources. In tests with lower process temperatures the first maximum was also lower.

0

The second group of AE signals is evidenced at the end of the constant drying period (II). Their number is the highest of the whole drying process. The reason for appearance of these signals can be explained by the tensional stresses that arise at the external layers of the cylinder as a result of shrinkage. The surface of the body becomes more and more dry while its core is kept still wet. The first cracks on the external surface of the cylinder are observed when the local stress reaches the yield strength or the strain exceeds the allowable ultimate limit.

The third (III) maximum of AE signals is rather of small or moderate magnitude and depends on drying conditions. It was stated that this maximum exists usually for high temperature or low humidity of the drying medium. It holds for porous materials revealing inelastic properties (e.g. wet wood, clay, kaolin), and can be explained as follows: at the beginning of drying, the external layers are stressed in tension and the core in compression. Inelastic strains occur both in the surface layer and in the wet core. Later, under a surface layer with reduced shrinkage, the core dries and attempts to shrink causing the stress state to reverse. These new induced tensional stresses in the core cause fracture of a brittle (almost dry) structure, in what follows generate the acoustic signals.

Fig. 9 presents the rate of AE hits for the five different temperatures of drying. It is seen that for the conditions of high drying rates created by high temperatures, the rate of AE hits achieve higher values than for lower temperatures. That high active emission of AE signals has a reflection in the drying induced stresses.

**Figure 9.** The rate of AE hits during drying at different temperatures

beginning of drying process, the second (II) one in the period when the surface layer intensively shrinks, and third (III) one is noticeable sometimes in the final stage of drying and identified

> II maximum

60 12

0

As the drying body is almost fully saturated at the initial stage of drying, the number and the maximum value of AE signals in the heating period (I) is proportional to temperature of the drying process. At this stage of drying, the thermal stresses dominate in the clay sample which are rather not so much meaningful. Unfortunately, at the initial stage of drying some of AE signals come from heating aluminium probe and the AE sensor. It is hard to decide which AE signals in this stage of drying are from the investigated kaolin-clay sample and which from other sources. In tests with lower process temperatures the first maximum was also lower.

The second group of AE signals is evidenced at the end of the constant drying period (II). Their number is the highest of the whole drying process. The reason for appearance of these signals can be explained by the tensional stresses that arise at the external layers of the cylinder as a result of shrinkage. The surface of the body becomes more and more dry while its core is kept still wet. The first cracks on the external surface of the cylinder are observed when the local

The third (III) maximum of AE signals is rather of small or moderate magnitude and depends on drying conditions. It was stated that this maximum exists usually for high temperature or low humidity of the drying medium. It holds for porous materials revealing inelastic properties (e.g. wet wood, clay, kaolin), and can be explained as follows: at the beginning of drying, the external layers are stressed in tension and the core in compression. Inelastic strains occur both in the surface layer and in the wet core. Later, under a surface layer with reduced shrinkage, the core dries and attempts to shrink causing the stress state to reverse. These new induced tensional stresses in the core cause fracture of a brittle (almost dry) structure, in what follows

stress reaches the yield strength or the strain exceeds the allowable ultimate limit.

AE hits rate

Drying kinetic

III maximum

5

15

10

Moisture content [%]

time [min]

as being generated by the reversed stresses.

I maximum

AE hits rate [N/30s]

10

**Figure 8.** AE signals and the curve of drying

generate the acoustic signals.

20

30

156 Acoustic Emission - Research and Applications

Note that the highest peak of the AE hits, which correspond to temperature 120o C, appears earlier than the lower peaks corresponding to the lower drying temperatures. The primary peak of AE hits appeared in 50 min of drying time, that is, when the tensional stresses at the cylinder surface reached maximum. The secondary peak is visible about 110 min drying time for temperature 120o C. In this time the core of the body starts to dry. The wet core wants to shrink but the surface layer is not able to deform itself because it is almost dry. So, in these circumstances the tensional stresses arise in a core. The secondary maximum is, of course, much lower than the first one.

Another AE descriptor termed *the total energy* is useful in analysis of fracture phenomenon by drying. It measures successively the total energy released during the whole process of drying. Figure 10 presents the rate of AE hits and the curve of total energy versus time.

The curve of summarized in time acoustic energy released during drying can deliver essential information on fracture dimension occurring in the investigated body. A highly fractured material is qualified as a bad quality one. The descriptor of total energy released may serve as an indicator, whether the dried product is of good or bad quality at the end-state. Strong cracks of body structure release high energetic EA signals. The high or medium energetic signals are evidenced in figure 10 as the strait upright lines. In some cases, the high energetic signals denote macro-cracks or splits that are visible on the sample surface. Taking into considerations the above presented curve of total energy one can state that it represents a dried product of bad quality.

The strength of material with damaged structure is impaired. Often a number of internal small micro-cracks arising during drying may nucleate and create macro-cracks during utilization

Analysing figure 11 one can see the differences in released energy for different drying

It means that drying at this temperature is unpropitious for creation of material fracture, so the manufactured product is of good quality and without residual stresses. Unfortunately, drying at such a low temperature takes a long time and is unsatisfactory from the economic

a greater number of signals in the time period from 40 to 100 min but these signals are not so much energetic either, some of them might be generated by invisible micro-cracks. This curve

energetic signals. The energy released here is much higher than that represented by the two previous curves. The dried product at this temperature may have a number of micro-cracks

The most energetic AE signals are represented by the curves characterizing severe drying

medium (air) are propitious to high drying rate. This produces quickly dry and brittle surface layers while the wet core becomes still wet and deformable. The drying induced stresses cause

Figure 12 presents the photographs of the cylindrical samples after microwave drying with 300 W of the microwave power (MWP). The damage of the cylinder is occurred in its center and looks like an explosion caused by a high vapor pressure inside the cylinder due to intensive phase transitions of water into vapor. This proofs that by microwave heating the highest temperature arises inside the material. It is confirmed by the picture presented in figure 12c

**Figure 12.** Kaolin sample subjected to 300 W MWP a) front view, b) rear view, c) IR camera picture of temperature

C) has similar character as that of 75o

that can expand into visible cracks under the action of residual stresses.

C, being almost horizontal, represents low energetic AE signals.

C looks a little different from that of 45o

C). So, high temperatures together with low humidity of the drying

C. It contains

159

C one, however, represents more

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796

conditions. The curve of 45 o

is slightly inclined upwards.

damage of the fragile surface.

The next curve (85o

conditions (100o

The total energy curve for drying at 75o

C and 120o

**5.2. Microwave drying of ceramic-like materials**

made with infrared (IR) camera (Flir therma-cam B2).

distribution in the sample longitudinal cross-section

point of view.

**Figure 10.** Total energy of AE signals released during drying

of dry products, so that an unpredictable total damage of the body may take place in any time after drying.

The reason for fracture of materials under drying results mostly from not proper drying conditions (e.g. too high temperature or too low drying medium humidity). By optimal drying process the high energetic AE signals ought to be eliminated or minimized. The majority of registered AE signals ought to be low energetic (horizontal or almost horizontal lines in figure 10). Low energetic AE signals mean lack of destruction in dried products.

Figure 11 shows several curves of total EA energy released from kaolin cylinders during drying at different temperatures. Each EA signal carries a certain portion of energy. The flat horizontal lines represent the low energetic signals. Hits of high energy create sudden vertical lines as, for example, those visible on the energy curves obtained for drying at temperatures 100 and 120o C. These very energetic signals are generated by strong material cracks.

**Figure 11.** Total energy of hits during drying process for various conditions

Analysing figure 11 one can see the differences in released energy for different drying conditions. The curve of 45 o C, being almost horizontal, represents low energetic AE signals. It means that drying at this temperature is unpropitious for creation of material fracture, so the manufactured product is of good quality and without residual stresses. Unfortunately, drying at such a low temperature takes a long time and is unsatisfactory from the economic point of view.

The total energy curve for drying at 75o C looks a little different from that of 45o C. It contains a greater number of signals in the time period from 40 to 100 min but these signals are not so much energetic either, some of them might be generated by invisible micro-cracks. This curve is slightly inclined upwards.

The next curve (85o C) has similar character as that of 75o C one, however, represents more energetic signals. The energy released here is much higher than that represented by the two previous curves. The dried product at this temperature may have a number of micro-cracks that can expand into visible cracks under the action of residual stresses.

The most energetic AE signals are represented by the curves characterizing severe drying conditions (100o C and 120o C). So, high temperatures together with low humidity of the drying medium (air) are propitious to high drying rate. This produces quickly dry and brittle surface layers while the wet core becomes still wet and deformable. The drying induced stresses cause damage of the fragile surface.

#### **5.2. Microwave drying of ceramic-like materials**

of dry products, so that an unpredictable total damage of the body may take place in any time

Total energy released curve

> Total energy

0,01

0,02

0,03

[(mV)2s]

medium energetic AE

60 120 time [min]

The reason for fracture of materials under drying results mostly from not proper drying conditions (e.g. too high temperature or too low drying medium humidity). By optimal drying process the high energetic AE signals ought to be eliminated or minimized. The majority of registered AE signals ought to be low energetic (horizontal or almost horizontal lines in figure

Figure 11 shows several curves of total EA energy released from kaolin cylinders during drying at different temperatures. Each EA signal carries a certain portion of energy. The flat horizontal lines represent the low energetic signals. Hits of high energy create sudden vertical lines as, for example, those visible on the energy curves obtained for drying at temperatures 100 and

10). Low energetic AE signals mean lack of destruction in dried products.

AE hits rate

C. These very energetic signals are generated by strong material cracks.

**Figure 11.** Total energy of hits during drying process for various conditions

after drying.

AE hits rate [N/30s]

10

**Figure 10.** Total energy of AE signals released during drying

20

30

158 Acoustic Emission - Research and Applications

120o

Figure 12 presents the photographs of the cylindrical samples after microwave drying with 300 W of the microwave power (MWP). The damage of the cylinder is occurred in its center and looks like an explosion caused by a high vapor pressure inside the cylinder due to intensive phase transitions of water into vapor. This proofs that by microwave heating the highest temperature arises inside the material. It is confirmed by the picture presented in figure 12c made with infrared (IR) camera (Flir therma-cam B2).

**Figure 12.** Kaolin sample subjected to 300 W MWP a) front view, b) rear view, c) IR camera picture of temperature distribution in the sample longitudinal cross-section

The sample subjected to 300 W of MWP has a huge vertical slit, almost 3 cm long and 2.5 cm deep (Fig. 12a). It was caused by the explosion after 20 min of drying time. The barrel shape of the samples is very clearly visible in both figures 12a and 12b. The picture of temperature distribution in the sample longitudinal cross-section (Fig. 12c) shows that the temperature reached about 90o C in some hot spots, although the mean temperature in the central part was about 72o C. This indicates the existence of places where the water was rapidly changed into vapor. The rapidly increased vapor pressure created the big slit, so that the water vapor had found the way out.

Figure 13 presents AE signals acquired during microwave drying of kaolin cylinders.

**Figure 13.** AE hits mean energy acquired during microwave drying of kaolin cylinders

For 300W of MWP the huge acoustic energy wave was generated during the explosion. It is visible in figure 13 as a high single signal at 20 minutes of drying. Drying with lower MWP (180 W and 240 W) induces signals of lower AE energy.

The number of AE signals induced during microwave drying depends on the MWP (Fig. 14). By lower MWP the number of AE signals is greater, but they are of lower energy than those of higher MWP.

dispersion of the high-frequency monochrome waves of the order 2.45 GHz. The microwave

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 161

The different ways of heat supply affects the directions of heat and mass fluxes. During the convective drying the heat flux is in opposite direction to the mass flux, and this causes a decrease of moisture removal, what is favorable to non-uniform distribution of the moisture inside the material. Such a negative thermodiffusive effect not appears or is minimal in microwave drying by which the heat flux coincides with the mass flux. The interior of the material usually has the temperature higher than the surroundings. The moisture distribution

power is absorbed mainly by the water present in the material pores.

**Figure 15.** The kaolin sample dried in microwave oven by 240 W MWP

**Figure 14.** AE results for microwave drying of kaolin: the total number of AE hits

The destruction of samples raised by microwave drying proceeds mainly in the second drying rate stage, except the one dried in 300W MWP. For samples dried in 180 W and 240W MWPs the destruction was observed as step by step splitting small parts (Fig. 15).

The convective and microwave drying methods differ from each other in the way of heat supply. By convective drying the heat is delivered from the surroundings through the material surface from the hot air of temperature higher than the temperature of drying material. By microwave drying on the other hand the heat is generated volumetrically as a result of the

**Figure 14.** AE results for microwave drying of kaolin: the total number of AE hits

The sample subjected to 300 W of MWP has a huge vertical slit, almost 3 cm long and 2.5 cm deep (Fig. 12a). It was caused by the explosion after 20 min of drying time. The barrel shape of the samples is very clearly visible in both figures 12a and 12b. The picture of temperature distribution in the sample longitudinal cross-section (Fig. 12c) shows that the temperature

vapor. The rapidly increased vapor pressure created the big slit, so that the water vapor had

Figure 13 presents AE signals acquired during microwave drying of kaolin cylinders.

**Figure 13.** AE hits mean energy acquired during microwave drying of kaolin cylinders

(180 W and 240 W) induces signals of lower AE energy.

For 300W of MWP the huge acoustic energy wave was generated during the explosion. It is visible in figure 13 as a high single signal at 20 minutes of drying. Drying with lower MWP

The number of AE signals induced during microwave drying depends on the MWP (Fig. 14). By lower MWP the number of AE signals is greater, but they are of lower energy than those

The destruction of samples raised by microwave drying proceeds mainly in the second drying rate stage, except the one dried in 300W MWP. For samples dried in 180 W and 240W MWP-

The convective and microwave drying methods differ from each other in the way of heat supply. By convective drying the heat is delivered from the surroundings through the material surface from the hot air of temperature higher than the temperature of drying material. By microwave drying on the other hand the heat is generated volumetrically as a result of the

s the destruction was observed as step by step splitting small parts (Fig. 15).

C in some hot spots, although the mean temperature in the central part was

C. This indicates the existence of places where the water was rapidly changed into

reached about 90o

160 Acoustic Emission - Research and Applications

found the way out.

of higher MWP.

about 72o

**Figure 15.** The kaolin sample dried in microwave oven by 240 W MWP

dispersion of the high-frequency monochrome waves of the order 2.45 GHz. The microwave power is absorbed mainly by the water present in the material pores.

The different ways of heat supply affects the directions of heat and mass fluxes. During the convective drying the heat flux is in opposite direction to the mass flux, and this causes a decrease of moisture removal, what is favorable to non-uniform distribution of the moisture inside the material. Such a negative thermodiffusive effect not appears or is minimal in microwave drying by which the heat flux coincides with the mass flux. The interior of the material usually has the temperature higher than the surroundings. The moisture distribution in the material in this case is more uniform than during the convective drying. So the drying induced stresses should be smaller. Diagrams get from measurements of total numbers of AE signals and the AE energy confirm this prediction.

energy. In the latter cases a number of distinct scratches was visible on the surface of dried samples and in some cases even micro and macro cracks were formed, particularly by drying

Figure 17 presents the AE hits in a kaolin sample that occurred by microwave drying. In the case of microwave drying one can see that both the number of AE hits (Fig. 17a) and the emitted AE energy (Fig. 17b) are much lower in comparison to the convective drying. The lower number of AE hits in microwave drying can be justified by the coincidence of the heat and mass fluxes and thus more uniform distribution of the moisture through the material in this kind of drying, which consequently resulted in reduction of stresses. It is important that a significant increase of drying rate was noticed by microwave drying. The CDRP amounted

Drying of pine wood samples was conducted at three different temperatures: 60, 80 and 100o

s) (Fig. 18a), and the drying curves (Fig. 18b), for different drying temperatures.

Figure 18 presents the rate of AE hits, i.e. the density of AE signals per a time interval (e.g. 30

One can state based on the experimental results that the rate of AE hits in drying wood depends on the number and size of fractures occurred in wood samples. The shrinkage of wood begins at the moment, when the MC reaches the fiber saturation point (FSP) (c.a. 30%). In the initial drying period, when the MC in wood is higher than FSP, the AE activity is insignificant. Only when MC in the surface layers drops below FSP, and the MC inside the samples exceeds this value, the AE start to reveal greater activity, in particular for high drying temperatures. The considerable increase of the rate of AE hits corresponds then with drying temperatures and is attested by drying stresses and cracks of different sizes, which generate acoustic wave.

C.

163

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796

in more severe drying conditions.

**5.3. Convective drying of wood**

only from 10 to 70 min (Fig. 17a), dependent on MWP.

**Figure 18.** Results of pine wood drying: a) the rate of AE hits, b) drying curves

Figure 16 presents the AE hits in kaolin sample dried convectively. It appears from the presented diagrams that the maximum number of AE signals was occurred in the middle of the constant drying rate period (CDRP).

**Figure 16.** The AE hits for convective drying of kaolin sample: a) drying curve and AE hits rate, b) mean energy of AE hits

Kaolin clay sustains the greatest shrinkage in the initial period of drying (15 – 90 min). External layers of the cylindrical sample are dried first and shrink generating stresses and cracks, which is manifested by an increased number of AE signals. The density of these signals decreases with the course of drying, however, their energy becomes more and more greater. Figures 16a presents the rate of AE hits and 16b the mean energy of AE hits. One can see that the initial great number of hits is of relatively low energy but the subsequent ones revealed much bigger

**Figure 17.** The AE hits by microwave drying of kaolin sample: a) drying curve and AE hits rate, b) mean energy of AE hits

energy. In the latter cases a number of distinct scratches was visible on the surface of dried samples and in some cases even micro and macro cracks were formed, particularly by drying in more severe drying conditions.

Figure 17 presents the AE hits in a kaolin sample that occurred by microwave drying. In the case of microwave drying one can see that both the number of AE hits (Fig. 17a) and the emitted AE energy (Fig. 17b) are much lower in comparison to the convective drying. The lower number of AE hits in microwave drying can be justified by the coincidence of the heat and mass fluxes and thus more uniform distribution of the moisture through the material in this kind of drying, which consequently resulted in reduction of stresses. It is important that a significant increase of drying rate was noticed by microwave drying. The CDRP amounted only from 10 to 70 min (Fig. 17a), dependent on MWP.

#### **5.3. Convective drying of wood**

in the material in this case is more uniform than during the convective drying. So the drying induced stresses should be smaller. Diagrams get from measurements of total numbers of AE

Figure 16 presents the AE hits in kaolin sample dried convectively. It appears from the presented diagrams that the maximum number of AE signals was occurred in the middle of

**Figure 16.** The AE hits for convective drying of kaolin sample: a) drying curve and AE hits rate, b) mean energy of AE

Kaolin clay sustains the greatest shrinkage in the initial period of drying (15 – 90 min). External layers of the cylindrical sample are dried first and shrink generating stresses and cracks, which is manifested by an increased number of AE signals. The density of these signals decreases with the course of drying, however, their energy becomes more and more greater. Figures 16a presents the rate of AE hits and 16b the mean energy of AE hits. One can see that the initial great number of hits is of relatively low energy but the subsequent ones revealed much bigger

**Figure 17.** The AE hits by microwave drying of kaolin sample: a) drying curve and AE hits rate, b) mean energy of AE

signals and the AE energy confirm this prediction.

the constant drying rate period (CDRP).

162 Acoustic Emission - Research and Applications

hits

hits

Drying of pine wood samples was conducted at three different temperatures: 60, 80 and 100o C. Figure 18 presents the rate of AE hits, i.e. the density of AE signals per a time interval (e.g. 30 s) (Fig. 18a), and the drying curves (Fig. 18b), for different drying temperatures.

**Figure 18.** Results of pine wood drying: a) the rate of AE hits, b) drying curves

One can state based on the experimental results that the rate of AE hits in drying wood depends on the number and size of fractures occurred in wood samples. The shrinkage of wood begins at the moment, when the MC reaches the fiber saturation point (FSP) (c.a. 30%). In the initial drying period, when the MC in wood is higher than FSP, the AE activity is insignificant. Only when MC in the surface layers drops below FSP, and the MC inside the samples exceeds this value, the AE start to reveal greater activity, in particular for high drying temperatures. The considerable increase of the rate of AE hits corresponds then with drying temperatures and is attested by drying stresses and cracks of different sizes, which generate acoustic wave.

The energy is released up to the end of drying process. Incurred earlier micro-cracks grow further. The mean energy of the AE hits is a measure of the progressing decomposition of wood. It is bigger for higher temperatures of the drying medium. However, in the conducted series of drying tests in no case occurred a visible crack of dried sample. This fact is evidenced on the curves of total energy presented in figure 21, that is, the energy summed up during the

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 165

One can see that the curves of total energy are growing smoothly, that is, without violent jumps. Violent jumps on graphs of total energy indicate just fractures visible even with a naked eye.

The phenomenon of stress reverse results from a constrained shrinkage in dried material. It can happen when the surface of material becomes deformed permanently due to intensive shrinkage. The stresses arose on the surface are tensional and in the material interior com‐ pressive ones. When the drying is progressing deeper into the material and its core starts to shrink but is hindered by the surface shell previously stretched in a permanent way, then the stress on the surface becomes compressive and that in the material interior tensional, so thus

Modeling of the stress reverse phenomenon requires taking into account inelastic properties of the materials. This issue was already considered in the work by [Kowalski, 2001, 2002;

Figure 22 shows the time evolution of the circumferential stresses distributed in the cylindrical kaolin sample dried convectively by the assumption that kaolin is viscoelastic and obey

We can see that the circumferential stresses are compressive in the core and tensional in the boundary layer at the beginning and reversibly signed at the end of the drying process. We suppose that the tensional stresses in the core of the cylinder may cause structural fracture and

Banaszak and Kowalski, 2002; Kowalski and Rajewska, 2002; Kowalski et al., 2002].

thus the emission of the third group of acoustic signals at the final stage of drying.

Maxwell model [Kowalski, 2001, 2002; Kowalski and Rajewska, 2002].

whole course of drying.

**Figure 21.** The total energy of AE hits for the whole drying process

**5.4. The phenomenon of stress reverse**

the stress reverse take place.

**Figure 19.** The energy of AE hits for pine wood sample dried at various temperatures

Figure 19 shows the energy of AE hits for pine wood for different drying temperatures. The graphs presented in this figure, and in particular this referring to drying at temperature 100o C, point out a danger of wood destruction at the moment when the AE energy reaches maximum.

As the plots in figure 19 show, the rate of AE hits grow rapidly at the beginning but after some time they decrease also quite rapidly. The rate of AE hits decreases in the period that refers to drying of the sample core, however, the emitted AE energy stays on the same level or even grows, as it is seen on the graph for 80o C. Incurred earlier micro-cracks start expanding and linking. A danger of wood destruction could be then even greater than in the initial period, because of increase of the summarized energy of AE signals. Dried wood becomes more and more rigid and the risk of the brittle cracking becomes more and more probable. Observation of the mean energy of AE hits presented in figure 20 confirms it.

**Figure 20.** The mean energy of AE hits

The energy is released up to the end of drying process. Incurred earlier micro-cracks grow further. The mean energy of the AE hits is a measure of the progressing decomposition of wood. It is bigger for higher temperatures of the drying medium. However, in the conducted series of drying tests in no case occurred a visible crack of dried sample. This fact is evidenced on the curves of total energy presented in figure 21, that is, the energy summed up during the whole course of drying.

**Figure 21.** The total energy of AE hits for the whole drying process

One can see that the curves of total energy are growing smoothly, that is, without violent jumps. Violent jumps on graphs of total energy indicate just fractures visible even with a naked eye.

#### **5.4. The phenomenon of stress reverse**

**Figure 19.** The energy of AE hits for pine wood sample dried at various temperatures

of the mean energy of AE hits presented in figure 20 confirms it.

100o

maximum.

grows, as it is seen on the graph for 80o

164 Acoustic Emission - Research and Applications

**Figure 20.** The mean energy of AE hits

Figure 19 shows the energy of AE hits for pine wood for different drying temperatures. The graphs presented in this figure, and in particular this referring to drying at temperature

As the plots in figure 19 show, the rate of AE hits grow rapidly at the beginning but after some time they decrease also quite rapidly. The rate of AE hits decreases in the period that refers to drying of the sample core, however, the emitted AE energy stays on the same level or even

linking. A danger of wood destruction could be then even greater than in the initial period, because of increase of the summarized energy of AE signals. Dried wood becomes more and more rigid and the risk of the brittle cracking becomes more and more probable. Observation

C, point out a danger of wood destruction at the moment when the AE energy reaches

C. Incurred earlier micro-cracks start expanding and

The phenomenon of stress reverse results from a constrained shrinkage in dried material. It can happen when the surface of material becomes deformed permanently due to intensive shrinkage. The stresses arose on the surface are tensional and in the material interior com‐ pressive ones. When the drying is progressing deeper into the material and its core starts to shrink but is hindered by the surface shell previously stretched in a permanent way, then the stress on the surface becomes compressive and that in the material interior tensional, so thus the stress reverse take place.

Modeling of the stress reverse phenomenon requires taking into account inelastic properties of the materials. This issue was already considered in the work by [Kowalski, 2001, 2002; Banaszak and Kowalski, 2002; Kowalski and Rajewska, 2002; Kowalski et al., 2002].

Figure 22 shows the time evolution of the circumferential stresses distributed in the cylindrical kaolin sample dried convectively by the assumption that kaolin is viscoelastic and obey Maxwell model [Kowalski, 2001, 2002; Kowalski and Rajewska, 2002].

We can see that the circumferential stresses are compressive in the core and tensional in the boundary layer at the beginning and reversibly signed at the end of the drying process. We suppose that the tensional stresses in the core of the cylinder may cause structural fracture and thus the emission of the third group of acoustic signals at the final stage of drying.

At the first stage of drying the stresses for the viscoelstic model run in a similar way as for the elastic model. After some time, however, when the dry zone extends deeper towards the wet core, the circumferential stresses start to change their sign at the boundary from tensional to compressive. Note that the maximum value of the tensional circumferential stresses is moving

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 167

Figure 24 shows the evolution of the number of AE hits in time during convective drying of kaolin sample. The plot of the number of AE hits is confronted with the curves presented the circumferential stresses determined on the basis of elastic Hooke model (dashed line) and

during drying from the boundary surface towards the interior of the cylinder.

**Figure 24.** Hits rate and the theoretical curve of circumferential stresses as a function of time

cause an increase of AE signals, which is visible in figure 24.

As it is seen from figure 24, Hooke model does not reflect the occurrence of stress re‐ verse. It demonstrates only the compliance of the first maximum of stresses with the in‐ creased AE activity. The consideration allow us to appreciate the meaning of the mathematical model adequacy for the description of the mechanical phenomena by dry‐ ing of wet materials. The experimentally observed enhanced emission of acoustic signals, betoken the enhanced destruction of the material at the final stage of drying. Using the elastic model one obtains the stress development, which rises from the beginning, then reaches a maximum at point in time, and next, as the process proceeds further, disap‐ pears totally. Otherwise, the model that takes into account the permanent deformations of the dried material, allows us as in the case of viscoelastic model to observe the stress re‐ verse, and in particular the appearance of tensional stresses inside the dried material at the final stage of drying. The tensional stresses in the core at the final stage of drying

viscoelastic Maxwell (solid line).

**Figure 22.** The evolution of circumferential stresses in kaolin cylinder dried convectively

Figure 23 shows the evolution of circumferential stresses in wooden cylinder (birch) dried convectively [Kowalski et al., 2002]. Here the Maxwell model was used for wood.

**Figure 23.** The evolution of circumferential stresses in wood cylinder dried convectively

At the first stage of drying the stresses for the viscoelstic model run in a similar way as for the elastic model. After some time, however, when the dry zone extends deeper towards the wet core, the circumferential stresses start to change their sign at the boundary from tensional to compressive. Note that the maximum value of the tensional circumferential stresses is moving during drying from the boundary surface towards the interior of the cylinder.

Figure 24 shows the evolution of the number of AE hits in time during convective drying of kaolin sample. The plot of the number of AE hits is confronted with the curves presented the circumferential stresses determined on the basis of elastic Hooke model (dashed line) and viscoelastic Maxwell (solid line).

**Figure 24.** Hits rate and the theoretical curve of circumferential stresses as a function of time

Figure 23 shows the evolution of circumferential stresses in wooden cylinder (birch) dried

t=120 min

r [mm]

20

t=10 min

t=30 min

t=1 min 0,8

t=60 min

**Figure 22.** The evolution of circumferential stresses in kaolin cylinder dried convectively

0,6

sjj [MPa]

166 Acoustic Emission - Research and Applications

0,4

0,2

0



convectively [Kowalski et al., 2002]. Here the Maxwell model was used for wood.

**Figure 23.** The evolution of circumferential stresses in wood cylinder dried convectively

As it is seen from figure 24, Hooke model does not reflect the occurrence of stress re‐ verse. It demonstrates only the compliance of the first maximum of stresses with the in‐ creased AE activity. The consideration allow us to appreciate the meaning of the mathematical model adequacy for the description of the mechanical phenomena by dry‐ ing of wet materials. The experimentally observed enhanced emission of acoustic signals, betoken the enhanced destruction of the material at the final stage of drying. Using the elastic model one obtains the stress development, which rises from the beginning, then reaches a maximum at point in time, and next, as the process proceeds further, disap‐ pears totally. Otherwise, the model that takes into account the permanent deformations of the dried material, allows us as in the case of viscoelastic model to observe the stress re‐ verse, and in particular the appearance of tensional stresses inside the dried material at the final stage of drying. The tensional stresses in the core at the final stage of drying cause an increase of AE signals, which is visible in figure 24.

### **6. Control of material damage with the help of AE**

#### **6.1. Avoiding material fractures through changes drying conditions**

The non-stationary (intermittent) convective drying denotes drying with different drying rates in several periods. The results of the drying studies presented in this chapter allows to state that the intermittent drying can be recommended above all to drying of materials, which have a tendency to cracking during drying as, for example, ceramics and wood. Through changes of drying conditions in the right moments one can avoid material fracture and thus preserve a good quality of dried products. Thus, one can state that intermittent drying positively influences the quality of the dried materials without significant extension of the drying time.

In these considerations the intermittent drying was realized through periodically changing both temperature and humidity of drying air. The results of intermittent drying are compared with adequate processes of stationary drying to show the profits resulting from the former.

Apart from the visual assessment of the quality of dried products, the acoustic emission (AE) method was applied for monitoring of the micro- and macro- cracks developed during drying [Kowalski and Pawłowski, 2010]. In those studies it was measured the total number of AE hits and the total amount of AE energy emitted. The descriptor of total AE energy is the sum of energy of all acoustic signals emitted by the dried sample from the beginning to the end of drying. It denotes the energy released due to material cracking. These descriptors show the moment at which the AE becomes intensive and how big is the AE intensity. Knowing these descriptors, one can assess the intensity of micro- and macro- cracks that arise in dried materials as well as their magnitude. The "intensity" quantifies the number of cracks per 30 s intervals or the total number of cracks in the whole process. The crack "magnitude" is evidenced as the vertical straight line on the descriptor of total AE energy curve. In this way we can estimate the degree of destruction and how fast the destruction advances in dried materials.

Figure 25 presents the total number of AE hits and the total AE energy emitted during stationary drying of the kaolin cylindrical sample at temperature 100 <sup>o</sup> C.

The plots in figure 25 show a continuous increase of the number of AE hits and the AE energy. The flatness on the energy plot that begins at about 100 min of drying follows from the release of the elastic energy accumulated in the stressed material due to material fracture, and in this way a reduction of the stress state occurs.

Figure 26 presents the total number of AE hits and the total AE energy emitted from the cylin‐ drical sample during drying with periodically changing temperature between 50 and 100 <sup>o</sup> C in the falling drying rate period (FDRP).

of drying is lesser than those during stationary drying and also lesser than during drying with

**Figure 26.** Total number of AE hits and total AE energy by convective drying of kaolin cylindrical sample at periodically

**Figure 25.** Total number of AE hits and total AE energy in stationary drying of cylindrical kaolin sample at 100oC

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 169

The humidification of air in the chamber dryer caused the plots of acoustic emission to be very rugged. Nevertheless, we can state that drying in intermittent conditions accomplished through periodically changing temperature and air humidity is accompanied by a smaller number of AE signals and smaller value of AE energy. This denotes less micro- and macro-

cracks in dried material and simultaneously better quality of dried products.

variable temperature.

changing temperature between 50 oC and 100 oC

Note that the total number of AE hits and the total AE energy emitted during drying with periodically changing temperature is less than those in stationary drying. The plots in figure 26 are not so smooth as those in figure 25, which follows from the variable air temperature, and strictly, by switching off and on the air heater and cooler.

Figure 27 presents the total number of AE hits and the total AE energy emitted during drying with variable air humidity. The number of emitted AE hits and the total AE energy in this kind

**6. Control of material damage with the help of AE**

168 Acoustic Emission - Research and Applications

**6.1. Avoiding material fractures through changes drying conditions**

destruction and how fast the destruction advances in dried materials.

stationary drying of the kaolin cylindrical sample at temperature 100 <sup>o</sup>

and strictly, by switching off and on the air heater and cooler.

way a reduction of the stress state occurs.

the falling drying rate period (FDRP).

Figure 25 presents the total number of AE hits and the total AE energy emitted during

The plots in figure 25 show a continuous increase of the number of AE hits and the AE energy. The flatness on the energy plot that begins at about 100 min of drying follows from the release of the elastic energy accumulated in the stressed material due to material fracture, and in this

Figure 26 presents the total number of AE hits and the total AE energy emitted from the cylin‐ drical sample during drying with periodically changing temperature between 50 and 100 <sup>o</sup>

Note that the total number of AE hits and the total AE energy emitted during drying with periodically changing temperature is less than those in stationary drying. The plots in figure 26 are not so smooth as those in figure 25, which follows from the variable air temperature,

Figure 27 presents the total number of AE hits and the total AE energy emitted during drying with variable air humidity. The number of emitted AE hits and the total AE energy in this kind

C.

C in

The non-stationary (intermittent) convective drying denotes drying with different drying rates in several periods. The results of the drying studies presented in this chapter allows to state that the intermittent drying can be recommended above all to drying of materials, which have a tendency to cracking during drying as, for example, ceramics and wood. Through changes of drying conditions in the right moments one can avoid material fracture and thus preserve a good quality of dried products. Thus, one can state that intermittent drying positively influences the quality of the dried materials without significant extension of the drying time. In these considerations the intermittent drying was realized through periodically changing both temperature and humidity of drying air. The results of intermittent drying are compared with adequate processes of stationary drying to show the profits resulting from the former. Apart from the visual assessment of the quality of dried products, the acoustic emission (AE) method was applied for monitoring of the micro- and macro- cracks developed during drying [Kowalski and Pawłowski, 2010]. In those studies it was measured the total number of AE hits and the total amount of AE energy emitted. The descriptor of total AE energy is the sum of energy of all acoustic signals emitted by the dried sample from the beginning to the end of drying. It denotes the energy released due to material cracking. These descriptors show the moment at which the AE becomes intensive and how big is the AE intensity. Knowing these descriptors, one can assess the intensity of micro- and macro- cracks that arise in dried materials as well as their magnitude. The "intensity" quantifies the number of cracks per 30 s intervals or the total number of cracks in the whole process. The crack "magnitude" is evidenced as the vertical straight line on the descriptor of total AE energy curve. In this way we can estimate the degree of

**Figure 25.** Total number of AE hits and total AE energy in stationary drying of cylindrical kaolin sample at 100oC

**Figure 26.** Total number of AE hits and total AE energy by convective drying of kaolin cylindrical sample at periodically changing temperature between 50 oC and 100 oC

of drying is lesser than those during stationary drying and also lesser than during drying with variable temperature.

The humidification of air in the chamber dryer caused the plots of acoustic emission to be very rugged. Nevertheless, we can state that drying in intermittent conditions accomplished through periodically changing temperature and air humidity is accompanied by a smaller number of AE signals and smaller value of AE energy. This denotes less micro- and macrocracks in dried material and simultaneously better quality of dried products.

Figure 28 presents the typical drying curve of clay samples with the CDRP (0 – 180 min) and the FDRP (180 – 400 min), and the descriptors of total AE energy and total number of AE signals

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 171

Each rapid increase of the total AE energy visible on the AE curve denotes a crack occurring of in the clay sample at a given moment. As seen in this figure, the biggest cracks were formed in the second stage of the CDRP and in the first stage of FDRP. At this stage the sample surface became dry while the core of the sample was still wet, and material cracks occurred at this

Figure 29 presents comparison of total AE energy for dried clay saturated with water contain‐

ing different concentration of surfactant SDS (0, 0.001, 0.01, 0.1 and 1%).

**Figure 29.** Comparison of total AE energy emitted by clay with different concentration of SDS

drying results are efficient is called *the critical micelle concentration* (CMC).

than that with 0% (pure water) concentration.

surface presented in figure 30b.

It is seen that different amounts of SDS added to water solutions used for clay saturation differentiate the total AE energy emitted by kaolin-clay during drying. As it follows from this figure, the greatest energy is emitted for clay saturated with pure water (0% SDS) and for the greatest surfactant concentration (1% SDS). It means that there is a SDS concentration at which the AE energy reaches minimum. The limit value of the surfactant concentration, at which the

Figure 30 shows that the quality of sample with 0.01% surfactant concentration is much better

Figure 30 proves that surfactant concentration of value close the CMC have a meaningful influence on moisture transport inside capillary-porous materials. These conclusion is confirmed by the good quality of dried product visualized on the photo of samples bottom

emitted during drying of clay saturated with pure water.

stage.

**Figure 27.** Total number of AE hits and total AE energy by convective drying of kaolin cylindrical sample at periodically changing air humidity between 4 % and 60 ÷ 80 %

#### **6.2. Reduction of material fractures through surfactant application**

In order to improve moisture transport inside the dried body, and thus to assure more uniform distribution of moisture in the material and thus avoid its cracking, the authors proposed wetting the raw kaolin-clay with water containing surface active agents (surfactants). These agents have the ability to stimulate the surface tension between water and the pore walls and thus to improve moisture transport inside the material [Cottrell, 1970; Wert & Thomson, 1974].

**Figure 28.** Drying curve, total AE energy and total number of AE hits in clay samples saturated with pure water of clay samples at air temperature 120 °C

Figure 28 presents the typical drying curve of clay samples with the CDRP (0 – 180 min) and the FDRP (180 – 400 min), and the descriptors of total AE energy and total number of AE signals emitted during drying of clay saturated with pure water.

Each rapid increase of the total AE energy visible on the AE curve denotes a crack occurring of in the clay sample at a given moment. As seen in this figure, the biggest cracks were formed in the second stage of the CDRP and in the first stage of FDRP. At this stage the sample surface became dry while the core of the sample was still wet, and material cracks occurred at this stage.

Figure 29 presents comparison of total AE energy for dried clay saturated with water contain‐ ing different concentration of surfactant SDS (0, 0.001, 0.01, 0.1 and 1%).

**Figure 29.** Comparison of total AE energy emitted by clay with different concentration of SDS

**6.2. Reduction of material fractures through surfactant application**

changing air humidity between 4 % and 60 ÷ 80 %

170 Acoustic Emission - Research and Applications

In order to improve moisture transport inside the dried body, and thus to assure more uniform distribution of moisture in the material and thus avoid its cracking, the authors proposed wetting the raw kaolin-clay with water containing surface active agents (surfactants). These agents have the ability to stimulate the surface tension between water and the pore walls and thus to improve moisture transport inside the material [Cottrell, 1970; Wert & Thomson, 1974].

**Figure 27.** Total number of AE hits and total AE energy by convective drying of kaolin cylindrical sample at periodically

**Figure 28.** Drying curve, total AE energy and total number of AE hits in clay samples saturated with pure water of clay

samples at air temperature 120 °C

It is seen that different amounts of SDS added to water solutions used for clay saturation differentiate the total AE energy emitted by kaolin-clay during drying. As it follows from this figure, the greatest energy is emitted for clay saturated with pure water (0% SDS) and for the greatest surfactant concentration (1% SDS). It means that there is a SDS concentration at which the AE energy reaches minimum. The limit value of the surfactant concentration, at which the drying results are efficient is called *the critical micelle concentration* (CMC).

Figure 30 shows that the quality of sample with 0.01% surfactant concentration is much better than that with 0% (pure water) concentration.

Figure 30 proves that surfactant concentration of value close the CMC have a meaningful influence on moisture transport inside capillary-porous materials. These conclusion is confirmed by the good quality of dried product visualized on the photo of samples bottom surface presented in figure 30b.

**Author details**

Stefan Jan Kowalski\*

**References**

(1).

44, 497-503.

berg.

Materials, AIChE Journal.

, Jacek Banaszak and Kinga Rajewska

Poznań University of Technology, Institute of Technology and Chemical Engineering, De‐

Acoustic Emission in Drying Materials http://dx.doi.org/10.5772/54796 173

[1] Berlinsky, Y. M, Rosen, J, & Simmons, J. Wadley HNG, ((1990). A Calibration Approach to Acoustic Emission Energy Measurement, Journal of Nondestructive Evaluation, 10

[2] Banaszak, J, & Kowalski, S. J. (2002). Drying induced stresses estimated on the base of

[3] Banaszak, J, & Kowalski, S. J. (2010). Acoustic methods in Engineering Applications,

[4] Banaszak, J, & Kowalski, S. J. (2005). Theoretical and experimental analysis of stresses and fractures in clay like materials during drying, Chem. Engineering and Processing, ,

[5] Cottrell, A. H. (1970). The mechanical properties of matter. Warsaw: PWN (in Polish).

[6] Kowalski, S. J. (2002). Theoretical study of stress reversal phenomena in drying of

[7] Kowalski, S. J. (2003). Thermomechanics of Drying Processes, Springer, Berlin, Heidel‐

[8] Kowalski, S. J. (2010). Control of mechanical processes in drying. Theory and Experi‐

[9] Kowalski, S. J, Banaszak, J, & Rybicki, A. (2012). Damage Analysis of Microwave-Dried

[10] Kowalski, S. J, & Musielak, G. (1999). Deformations and stresses in dried wood.

[11] Kowalski, S. J, Molinski, W, & Musielak, G. (2004). Identification of fracture in dried wood based on theoretical modeling and acoustic emission. Wood Sci Techn , 38, 35-52.

[12] Kowalski, S. J, & Pawlowski, A. (2010). Drying of wet materials in intermittent condi‐

\*Address all correspondence to: stefan.j.kowalski@put.poznan.pl

elastic and viscoelastic models. Chem Eng J , 86, 139-143.

Publisher: Poznań University of Technology, Poznań (in Polish).

porous media. Dev. Chem. Eng. Mineral Process., 10(3/4): 261-280.

ment, Chemical Engineering Science, , 65, 890-899.

Transport in Porous Media , 34, 239-248.

tions,, Drying Technol. 28 (5). , 636-643.

partment of Process Engineering, Poznań, Poland

**Figure 30.** Bottom surface of kaolin-clay samples after drying: a) without surfactant, b) with 0.01% of SDS

#### **7. Conclusions**

The presented in this chapter results of research concerning analysis of the AE activity in dried materials allows to state that the AE method can indeed support the control of the drying process and facilitate the guidance for the purpose of avoiding destruction of materials during drying. Comparison of the drying induce stresses simulated numerically on the basis of mechanistic drying model with the experimentally measured descriptors of AE activity reveal an excellent adherence of the theoretical and experimental results. Although with the AE method we are not able to estimate strictly the magnitude of generated stresses, however, the assessment of material destruction intensity caused by the stresses and the time and place of their occurrence is very helpful for control of drying processes. Besides, monitoring of the AE events can be helpful also for validation of the failure criterion formulated on the basis of the mechanistic theory of drying, which is used for estimation of the magnitude and location of maximal stresses as well as their time evolution during drying.

It is worth to point out here the importance of the acoustic emission method that allows us observations *on line*the development of the acoustic signals connected with the destruction of the materials. The possibility of the registration of various descriptors such as: the intensity of acoustic signals, the energy of emitted signals and the total number of signals or total amount of energy allows the current control of drying processes.

### **Acknowledgements**

This work was carried out as a part of research project No. N N209 031638 and N N209 104337 sponsored by the Polish Ministry of Education and Science.

#### **Author details**

Stefan Jan Kowalski\* , Jacek Banaszak and Kinga Rajewska

\*Address all correspondence to: stefan.j.kowalski@put.poznan.pl

Poznań University of Technology, Institute of Technology and Chemical Engineering, De‐ partment of Process Engineering, Poznań, Poland

#### **References**

**7. Conclusions**

172 Acoustic Emission - Research and Applications

**Acknowledgements**

The presented in this chapter results of research concerning analysis of the AE activity in dried materials allows to state that the AE method can indeed support the control of the drying process and facilitate the guidance for the purpose of avoiding destruction of materials during drying. Comparison of the drying induce stresses simulated numerically on the basis of mechanistic drying model with the experimentally measured descriptors of AE activity reveal an excellent adherence of the theoretical and experimental results. Although with the AE method we are not able to estimate strictly the magnitude of generated stresses, however, the assessment of material destruction intensity caused by the stresses and the time and place of their occurrence is very helpful for control of drying processes. Besides, monitoring of the AE events can be helpful also for validation of the failure criterion formulated on the basis of the mechanistic theory of drying, which is used for estimation of the magnitude and location of

**Figure 30.** Bottom surface of kaolin-clay samples after drying: a) without surfactant, b) with 0.01% of SDS

It is worth to point out here the importance of the acoustic emission method that allows us observations *on line*the development of the acoustic signals connected with the destruction of the materials. The possibility of the registration of various descriptors such as: the intensity of acoustic signals, the energy of emitted signals and the total number of signals or total amount

This work was carried out as a part of research project No. N N209 031638 and N N209 104337

maximal stresses as well as their time evolution during drying.

of energy allows the current control of drying processes.

sponsored by the Polish Ministry of Education and Science.


[13] Kowalski, S. J, & Rajewska, K. (2002). Dried induced stresses in elastic and viscoelastic saturated materials. Chem Eng Sci , 57, 3883-3892.

**Chapter 8**

**Application of Acoustic Emission for**

Additional information is available at the end of the chapter

Artur Zdunek

**1. Introduction**

sound duration in time.

phenomena, i.e. is the bone-conducted sound [4].

http://dx.doi.org/10.5772/53985

**Quality Evaluation of Fruits and Vegetables**

Food crushing sound is one of the main factors used for food quality evaluation. Crispness and crunchiness are attributes of high quality product and are usually pointed on the top of a list of consumer preferences. However, the meanings of crispness and crunchiness are still imprecise. Its perception varies from country to country and from individual to individual. Despite of this there is a general consensus that crispy and crunchy sensation is related to fracture properties. Crispy product is mechanically brittle, firm and acoustically noisy as a result of large number of small fractures. Crunchiness is probably related to events (frac‐ tures) occurring on subsequent layers in a cell structure what gives the sense of extension of

In spite of sensory and subjective nature of food quality evaluation by human senses, a big effort is put for objective sound properties analysis during biting and chewing and for de‐ veloping instrumental methods for human independent food evaluation. The first instru‐ mental analysis of sound was published by Drake in 1963, who found that crisper products emit louder sound and an average amplitude of successive bursts during mastication de‐ creases [1]. Then, several authors used different sound descriptors for judging a chewing sound, as the number of sound burst in a bite *n*, the mean amplitude of the burst *A* or the products of these values *nA* or *nA/sound duration* [1, 2, 3]. The first hypothesis was that the sense of crispness is an auditory phenomena, i.e. is the air-conducted sound. However, work done by Christensen and Vickers in 1981 showed that crispness may be a vibratory

Most of studies on crispness and crunchiness concern dry food products, like cakes, chips, etc.. However, this problem has been found as important also for fruits and vegetables called as wet food products. In 2002, presumably for the first time, Fillion and Kilcast stated

> © 2013 Zdunek; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 Zdunek; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution

distribution, and reproduction in any medium, provided the original work is properly cited.

License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

