**7. References**


The authors would like to thank Mr. Dimitris Roulias for his valuable assistance with many

Abbasion, S.; Rafsanjani, A.; Farshidianfar, A.; Irani, N. (2007). Rolling element bearings

Anami, B.; Pagi, V.; Magi, S. (2011). Wavelet-based acoustic analysis for determining health

Antonino-Daviu, J.; Jover, P. ; Riera, M.; Arkkio, A.; Roger-Folch, J. (2007). DWT analysis of

Bao, W.; Zhou, R.; Yang, J.; Yu, D.; Li, N. (2009). Anti-aliasing lifting scheme for mechanical

Belotti, V.; Crenna, F.; Michelini, R.C.; Rossi, G.B. (2006). Wheel-flat diagnostic tool via

Belsak, A.; Flasker, J. (2009). Wavelet analysis for gear crack identification, *Engineering* 

Borghetti, A.; Corsi, S.; Nucci, C.A.; Paolone, M.; Peretto, L.; Tinarelli, R. (2006). On the use

Cai, T.T.; Silverman, B.W. (2001). Incorporating information on Neighboring Coefficients

Carneiro, A.; da Silva, A.; Upadhyaya, B.R. (2008). Incipient fault detection of motor-

Chen, H.X.; Chua, P.S.K.; Lim, G.H. (2006). Adaptive wavelet transform for vibration signal

*Systems and Signal Processing*, Vol.20, No.8, (November 2006), pp. 2022-2045 Chen, H.X.; Chua, P.S.K.; Lim, G.H. (2007). Vibration analysis with lifting scheme and

Chen, H.X.; Chua, P.S.K.; Lim, G.H. (2008). Fault degradation assessment of water hydraulic

*Sound and Vibration*, Vol.301, No.3-5, (April 2007), pp. 458-480

wavelet transform, *Mechanical Systems and Signal Processing*

*Failure Analysis*, Vol.16, No.6, (September 2009), pp. 1983-1990

multi-fault classification based on the wavelet denoising and support vector machine, *Mechanical Systems and Signal Processing,* Vol.21, No.7, (October 2007), pp.

condition of motorized two-wheelers. *Applied Acoustics*, Vol.72, No.7, (June 2011),

numerical and experimental data for the diagnosis of dynamic eccentricities in induction motors, *Mechanical Systems and Signal Processing*, Vol.21, No.6, (August

vibration fault feature extraction, *Mechanical Systems and Signal Processing*, Vol.23,

of continuous-wavelet transform for fault location in distribution power systems, International *Journal of Electrical Power & Energy Systems*, Vol.28, No.9, (November

into wavelet estimation, *Sankhya: The Indian Journal of Statistics Series B*, Vol.63,

operated valves using wavelet transform analysis, *Nuclear Engineering and Design*,

modelling and application in fault diagnosis of water hydraulic motor, *Mechanical* 

generalized cross validation in fault diagnosis of water hydraulic system, *Journal of* 

motor by impulse vibration signal with Wavelet Packet Analysis and Kolmogorov– Smirnov Test, *Mechanical Systems and Signal Processing*, Vol.22, No.7, (October 2008),

**6. Acknowledgements** 

of the figures of this work.

2933-2945

pp. 464-469

2007), pp. 2575-2589

2006), pp. 608-617

Pages 1670-1684

No.2, (2001) pp. 127–148.

Vol.238, No.9, (September 2008), pp. 2453-2459

No.5, (July 2009), pp. 1458-1473

**7. References** 


Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications 309

Lin, J.; Liu, J.; Li, C.; Tsai, L.; Chung, H. (2010). Motor shaft misalignment detection using

Loutas, T.H.; Sotiriades, G.; Kalaitzoglou, I.; Kostopoulos, V. (2009). Condition monitoring of

Loutas, T.H.; Roulias, D.; Pauly, E.; Kostopoulos, V. (2011).The combined use of vibration,

Mallat, S. (1989), "A theory for multiresolution signal decomposition: The wavelet

Miao, Q.; Makis, V. (2007). Condition monitoring and classification of rotating machinery

Monsef, H.; Lotfifard, S. (2007). Internal fault current identification based on wavelet

Niu, G.; Widodo, A.; Son, J.; Yang, B.; Hwang, D.; Kang, D. (2008). Decision-level fusion

Niu, G.; Yang, B. (2010). Intelligent condition monitoring and prognostics system based on

Ocak, H.; Loparo, K.A.; Discenzo, F.M. (2007). Online tracking of bearing wear using

Pan, Y.; Chen, J.; Guo, L. (2009). Robust bearing performance degradation assessment

Peng, Z.K.; Chu, F.L.; Tse, P.W. (2007). Singularity analysis of the vibration signals by means

Purushotham, V.; Narayanan, S.; Prasad, S.A.N. (2005). Multi-fault diagnosis of rolling

*Processing*, Vol.24, No.2, (February 2010), pp. 559-566

Vol.21, No.2, (February 2007), pp. 780-794

No.10, (October 2010), pp. 7200-7204

Vol.41, No.7, (July 2010), pp. 10-18

No.7, (July 1989), pp. 674–693.

2006), pp. 1953-1966

2008), pp. 918-928

2010), pp. 8831-8840

*October 2007*, pp. 1637-1645

*Processing*, Vol.25, No.4, (May 2011), pp. 1339-1352

multiscale entropy with wavelet denoising, *Expert Systems with Applications*, Vol.37,

a single-stage gearbox utilizing on-line vibration and acoustic emission measurements, *Applied Acoustics,* Vol.70, No.9, (September 2009), pp. 1148-1159 Loutas, T.H.; Kostopoulos, V. (2010). Wavelet-based methodologies for the analysis of

vibration recordings for fault diagnosis in gears, *Noise and Vibration Worldwide,*

acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery, *Mechanical Systems and Signal* 

representation, *IEEE Transactions on Pattern Analysis and Machine Intelligence*, Vol.11,

using wavelets and hidden Markov models, *Mechanical Systems and Signal Processing*, Vol.21, No.2, (February 2007), pp. 840-855 , Vol.20, No.8, (November

transform in power transformers, *Electric Power Systems Research*, Vol.77, No.12*,* 

based on wavelet decomposition for induction motor fault diagnosis using transient current signal, *Expert Systems with Applications*, Vol.35, No.3, (October

data-fusion strategy, *Expert Systems with Applications*, Vol.37, No.12, (December

wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics, *Journal of Sound and Vibration*, Vol.302, No.4-5, (May 2007), pp. 951-961

method based on improved wavelet packet–support vector data description, *Mechanical Systems and Signal Processing*, Vol.23, No.3, (April 2009), pp. 669-681 Pan, Y.; Chen, J.; Li, X. (2010). Bearing performance degradation assessment based on lifting

wavelet packet decomposition and fuzzy c-means, *Mechanical Systems and Signal* 

of wavelet modulus maximal method, *Mechanical Systems and Signal Processing*,

bearing elements using wavelet analysis and hidden Markov model based fault recognition, *NDT & E International*, Vol.38, No.8, (December 2005), pp. 654-664


Hong, H.; Liang, M. (2009). Fault severity assessment for rolling element bearings using the

Hu, Q.; He, Z.; Zhang, Z.; Zi, Y. (2007). Fault diagnosis of rotating machinery based on

Huang, Y.; Liu, C.; Zha, X.F.; Li, Y. (2010). A lean model for performance assessment of

*Expert Systems with Applications,* Vol.37, No.5, (May 2010), pp. 3815-3822 Hyvarinen, A. (1999). Sparse code shrinkage: denoising of non-gaussian data by maximum likelihood estimation, *Neural Comput,* Vol.11, No.7, (October 1999), pp. 1739–1768 Jafarizadeh, M.A.; Hassannejad, R.; Ettefagh, M.M.; Chitsaz, S. (2008). Asynchronous input

*Systems and Signal Processing*, Vol.22, No.1, (January 2008), pp. 172-201 Jazebi, S.; Vahidi, B.; Jannati, M. (2011). A novel application of wavelet based SVM to

Kankar, P.; Sharma, S.; Harsha, S. (2011). Fault diagnosis of ball bearings using continuous

Kar, C.; Mohanty, A.R. (2008). Vibration and current transient monitoring for gearbox fault

Kingsbury, N.G. (1998). The dual-tree complex wavelet transform: A new technique for shift

Lei, Y.; He, Z.; Zi, Y. (2009). Application of an intelligent classification method to mechanical

Lei, Y.; He, Z.; Zi, Y. (2011). EEMD method and WNN for fault diagnosis of locomotive

Li, W.; Gong, W.; Obikawa, T.; Shirakashi, T. (2005). A method of recognizing tool-wear

Li, Z.; He, Z.; Zia, Y.; Jiang, H. (2008). Rotating machinery fault diagnosis using signal-

Li, H.; Zhang, Y.; Zheng, H. (2011). Application of Hermitian wavelet to crack fault detection

Liao, T. W.; Ting, C.; Qu, J.; Blau, P.J. (2007). A wavelet-based methodology for grinding

Processing Technology, Vol.170, No.1-2, (December 2005), pp. 374-380 Li, L.; Qu, L.; Liao, X. (2007). Haar wavelet for machine fault diagnosis, *Mechanical Systems* 

*and Signal Processing*, Vol.21, No.4, (May 2007), pp. 1773-1786

*Vibration*, Vol.320, No.1-2, (February 2009), pp. 452-468

*Signal Processing*, Vol.21, No.2, (February 2007), pp. 688-705

*Management*, Vol. 52, No.2, (February 2011), pp. 1354-1363

Vol.311, No.1-2, (March 2008), pp. 109-132

Aug. 9–12, 1998, paper no. 86.

(April 2008), pp. 542-556

Vol.47, No.3-4, (March 2007), pp. 580-592

2312

9941-9948

1353-1363

7341

Lempel–Ziv complexity and continuous wavelet transform, *Journal of Sound and* 

improved wavelet package transform and SVMs ensemble, *Mechanical Systems and* 

machinery using second generation wavelet packet transform and Fisher criterion,

gear damage diagnosis using time averaging and wavelet filtering, *Mechanical* 

transient phenomena identification of power transformers*, Energy Conversion and* 

wavelet transform, *Applied Soft Computing*, Vol.11, No.2, (March 2011), pp. 2300-

detection using multi-resolution Fourier transform, *Journal of Sound and Vibration*,

invariance and directional filters, in *Proceedings of the 8th IEEE DSP Workshop*, Utah,

fault diagnosis, *Expert Systems with Applications*, Vol.36, No.6, (August 2009), pp.

roller bearings, *Expert Systems with Applications*, Vol.38, No.6, (June 2011), pp. 7334-

states based on a fast algorithm of wavelet transform, Journal of Materials

adapted lifting scheme, *Mechanical Systems and Signal Processing*, Vol.22, No.3,

in gearbox, *Mechanical Systems and Signal Processing*, Vol.25, No.4, (May 2011), pp.

wheel condition monitoring, *International Journal of Machine Tools and Manufacture*,


Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications 311

Singh, G.K.; Kazzaz, S. (2009). Isolation and identification of dry bearing faults in induction

Sweldens, W. (1998). The lifting scheme: A construction of second generation wavelets, *SIAM Journal on Mathematical Analysis,* Vol.29, No.2, (March 1998) pp. 511-546 Tang, B.; Liu, W.; Song, T. (2011). Wind turbine fault diagnosis based on Morlet wavelet

Velayudham, A.; Krishnamurthy, R.; Soundarapandian, T. (2005). Acoustic emission based

Wang, D.; Miao, Q.; Kang, R. (2009). Robust health evaluation of gearbox subject to tooth

Wang, Z.; Jiang, H. (2010). Robust incipient fault identification of aircraft engine rotor based

Wang, X.; Makis, V.; Yang, M. (2010). A wavelet approach to fault diagnosis of a gearbox

Wang, W.; Kanneg, D. (2009). An integrated classifier for gear system monitoring, *Mechanical Systems and Signal Processing*, Vol.23, No.4, (May 2009), pp. 1298-1312 Wang, Y.; He, Z.; Zi, Y. (2010). Enhancement of signal denoising and multiple fault

Widodo, A.; Yang, B. (2008). Wavelet support vector machine for induction machine fault

Wu, J.; Chen, J. (2006). Continuous wavelet transform technique for fault signal diagnosis of

Wu, J.; Liu, C. (2008). Investigation of engine fault diagnosis using discrete wavelet

Wu, J.; Hsu, C. (2009). Fault gear identification using vibration signal with discrete wavelet

Wu, J.; Chan, J. (2009). Faulted gear identification of a rotating machinery based on wavelet

Wu, J.; Liu, C. (2009). An expert system for fault diagnosis in internal combustion engines

*Applications*, Vol.36, No.3, Part 1, (April 2009), pp. 4278-4286

pp. 849-861

pp. 221-224

pp. 119-137

304-311

2010), pp. 1570-1585

(December 2010), pp. 2862-2866

2, (December 2005), pp. 141-145

No.1-2, (July-August 2008), pp. 307-316

Vol.36, No.2, Part 2, (March 2009), pp. 3785-3794

(October 2008), pp. 1200-1213

No.5, (July 2009), pp. 8862-8875

(July 2009), pp. 1141-1157

machine using wavelet transform, *Tribology International*, Vol.42, No.6, (June 2009),

transformation and Wigner-Ville distribution, *Renewable Energy*, Vol.35, No.12,

drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform*, Materials Science and Engineering: A*, Vol.412, No.1-

failure with wavelet decomposition, *Journal of Sound and Vibration*, Vol.324, No.3-5,

on wavelet and fraction, *Aerospace Science and Technology*, Vol.14, No.4, (June 2010),

under varying load conditions, *Journal of Sound and Vibration*, Vol.329, No.9, (April

signatures detecting in rotating machinery using dual-tree complex wavelet transform, *Mechanical Systems and Signal Processing*, Vol.24, No.1, (January 2010),

diagnosis based on transient current signal, *Expert Systems with Applications*, Vol.35,

internal combustion engines, *NDT & E International*, Vol.39, No.4, (June 2006), pp.

transform and neural network, *Expert Systems with Applications*, Vol.35, No.3,

transform technique and fuzzy–logic inference, *Expert Systems with Applications*,

transform and artificial neural network, *Expert Systems with Applications*, Vol.36,

using wavelet packet transform and neural network, *Expert Systems with* 


Vol.36, No.5, (July 2009), pp. 9564-9573


Qiu, H.; Lee, J.; Lin, J.; Yu, G. (2006). Wavelet filter-based weak signature detection method

Rafiee, J.; Arvani, F.; Harifi, A.; Sadeghi, M.H. (2007). Intelligent condition monitoring of a

Rafiee, J.; Tse, P.W. (2009). Use of autocorrelation of wavelet coefficients for fault diagnosis, *Mechanical Systems and Signal Processing*, Vol.23, No.5, (July 2009), pp. 1554-1572 Rafiee, J.; Tse, P.W.; Harifi, A.; Sadeghi, M.H. (2009). A novel technique for selecting mother

Rafiee, J.; Rafiee, M.A.; Tse, P.W. (2010). Application of mother wavelet functions for

Randall, R.; Antoni, J. (2011). Rolling element bearing diagnostics - A tutorial, *Mechanical Systems and Signal Processing,* Vol.25, No.2, (February 2011), pp. 485-520 Sanz, J.; Perera, R.; Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-

Saravanan, N.; Siddabattuni, V.N.S.; Ramachandran, K.I. (2008). A comparative study on

Saravanan, N.; Ramachandran, K.I. (2009). Fault diagnosis of spur bevel gear box using

Saravanan, N.; Ramachandran, K.I. (2009). A case study on classification of features by fast

Saravanan, N.; Ramachandran, K.I. (2010). Incipient gear box fault diagnosis using discrete

Sawalhi, N.; Randall, R. (2011). Vibration response of spalled rolling element bearings:

Su, W.; Wang, F.; Zhu, H.; Zhang, Z.; Guo, Z. (2010). Rolling element bearing faults

*Tools & Manufacture*, Vol.51, No.9 (September 2011), pp. 701–710

*Vibration*, Vol.289, No.4-5, (February 2006), pp. 1066-1090

*Applications*, Vol.36, No.3, Part 1, (April 2009), pp. 4862-4875

Vol.21, No.4, (May 2007), pp. 1746-1754

No.6, (June 2010), pp. 4568-4579

Vol.302, No.4-5, (May 2007), pp. 981-999

No.3, (October 2008), pp. 1351-1366

Vol.36, No.5, (July 2009), pp. 9564-9573

*Applications*,

pp. 4168-4181

10862

and its application on rolling element bearing prognostics, *Journal of Sound and* 

gearbox using artificial neural network, *Mechanical Systems and Signal Processing*,

wavelet function using an intelligent fault diagnosis system, *Expert Systems with* 

automatic gear and bearing fault diagnosis, *Expert Systems with Applications*, Vol.37,

associative neural networks and wavelet transforms, *Journal of Sound and Vibration*,

classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box, *Expert Systems with Applications*, Vol.35,

discrete wavelet features and Decision Tree classification, *Expert Systems with* 

single-shot multiclass PSVM using Morlet wavelet for fault diagnosis of spur bevel gear box, *Expert Systems with Applications*, Vol.36, No.8, (October 2009), pp. 10854-

wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN), *Expert Systems with Applications*, Vol.37, No.6, (June 2010),

Observations, simulations and signal processing techniques to track the spall size, *Mechanical Systems and Signal Processing*, Vol.25, No.3, (April 2011), pp. 846-870 Shao, H.; Shi, X.; Li, L. (2011). Power signal separation in milling process based on wavelet

transform and independent component analysis. *International Journal of Machine* 

diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement, *Mechanical Systems and Signal Processing*, Vol.24, No.5, (July 2010), pp. 1458-1472


**14** 

*Oman* 

Khalid Al-Raheem

*Caledonian College of Engineering* 

**Wavelet Analysis and Neural** 

**Networks for Bearing Fault Diagnosis** 

The manufacturing productivity can be achieved through the availability of the physical resources and improved manufacturing methods and technology. The operational availability of various industrial systems can be increased by adopting efficient maintenance strategies. An ideal maintenance strategy meets the requirements of machine availability

Today, most maintenance actions are carried out by either corrective (run to failure) or preventive (scheduled or predetermined) strategy. In Corrective Maintenance (CM) the components are maintained after obvious faults or actual breakdown has occurred. With this maintenance strategy the associated costs are usually high due to the production losses, fault occurrence damages, restoring equipment until is being used at failure condition, and the safety/health hazards presented by the fault. However, the Preventive Maintenance (PM) approach has been developed to overcome the CM deficiencies. Traditionally, PM is a time driven process which is performed at regular time intervals, commonly termed the maintenance cycle, regardless of the components actual condition, in order to prevent component or systems breakdown. For example, changing the car engine oil at every 5000 KMs traveled distance, where no concern as to the actual condition and performance

Over recent decades some industries have started to employ a second type of PM actions in a predictive manner, where the actual machinery condition is the key indicator for the maintenance schedule and appropriate maintenance tasks (condition driven), therefore

In CBM systems, the machinery condition assessment is achieved by acquiring and interpreting the actual machine data continuously with an aim to provide lead-time and required maintenance prior to predicted failure or loss of efficiency (Just-In-Time maintenance). The application of the CBM approach provides the ability to optimize the availability of process machinery, and greatly reduce the cost of maintenance. The CBM system also provides the means to improve product quality, productivity, profitability,

The tools and techniques employed in the field of the CBM systems include: measurement and sensor technology, modeling of failure mechanisms, failure forecasting techniques,

safety and overall effectiveness of manufacturing and production plant.

**1. Introduction** 

and operational safety at minimum cost.

capability of the replaced oil.

referred to as Condition Based Maintenance (CBM).

