**1. Introduction**

486 Induction Motors – Modelling and Control

(April 2002), 465-475

(April 2003), 322–327

Germany

100–110.

processing and control engineering, Germany

signal processing and control engineering, Germany

signal processing and control engineering, Germany

*Industrial Electronics*, Vol.47, No.2, (Apr 2000), 356–367.

*MechRob-2004*, 378–382, Aachen, Germany, September 2004

*IECON'05*, 906–911, Raleigh North-Carolina, November 2005.

*Industrial Electronics*, Vol. 49, No. 2, (Apr 2002), 444–455.

4219, Aachen, Germany, 2004.

Bouchhida, O. (2011). *Etude et Optimisation des Performances d'Onduleur Monophasé et Triphasé à Commutation Pré-Calculée*. Habilitation Universitaire, Université Saad D'hleb Blida, Algérie Czarkowski, D.; Chudnovsky, D.; Chudnovsky, G. & Selesnick, I. (2002). Solving the optimal PWM problem for Single-phase inverters. *IEEE Transactions Circuits Syst*. I, Vol. 49,

dSPACE (2006a). *DS1103 PPC Controller Board, Feature, Realise 5.1*, dSPACE digital signal

dSPACE (2006b). *How to implement user-specific functions on the DS1103 slave DSP (TMS320F240).* dSPACE digital signal processing and control engineering GmbH,

dSPACE (2006c). *DS1103 PPC Controller Board, Hardware Installation and Configuration, Realise* 

dSPACE (2006d). *DS1103 PPC Controller Board RTI Reference, Realise 5.1*, dSPACE digital

dSPACE (2006e). *DS1103 PPC Controller Board RTLib Reference, Realise 5.1*, dSPACE digital

García, O.; Martínez-Avial, M.D.; Cobos, A.; Uceda, J.; González, J. & Navas, A. (2003). Harmonic reducer converter. *IEEE Transactions on Industrial Electronics*, Vol. 50, No. 2,

Jung, J. & Nam, K. (1999). A dynamic decoupling control scheme for high speed operation of induction motors. *IEEE Transaction on Industrial Electronics, V*ol.46, No. 1, (Feb 1999),

Lin, F.J.; Wai, R.J.; Lin, C.H.; & Liu, D.C. (2000). Decoupling stator-flux oriented induction motor drive with fuzzy neural network uncertainly observer. *IEEE Transaction on* 

Meghriche, K.; Chikhi, F. & Cherifi, A. (2004). A new switching angle determination method for three leg inverter, *Proceedings of International IEEE Mechatronics and Robotics* 

Meghriche, K.; Mansouri, O. & Cherifi, A. (2005). On the use of pre-calculated switching angles to design a new single phase static PFC inverter, *Proceedings of the 31st IEEE* 

Suwankawin, S. ; & Sangwongwanich, S. (2002). A speed sensorless IM drive with decoupling control and stability analysis of speed estimation. *IEEE Transaction on* 

Villarreal-Ortiz, R.A.; Hernández-Angeles, M.; Fuerte-Esquivel, C.R. & Villanueva-Chávez, R.O. (2005). Centroid PWM technique for inverter harmonics elimination. *IEEE* 

Wells, J.R.; Nee, B.M.; Chapman, P.L. & Krein, P.T. (2004). Optimal Harmonic elimination control, *Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference*, 4214-

*Transactions on Industrial Electronics*, Vol. 20, No. 2, (April 2005), 1209–1210

Davis, L. (1991). *The Handbook of Genetic Algorithms*, Van Nostrand & Reinhold, 1991

*5.1*, dSPACE digital signal processing and control engineering, Germany

Industries always try to increase the reliability of their productive process. In this context, predictive maintenance performs a fundamental role in order to reach high availability and reliability concerning their pieces of equipment. Predictive maintenance can be understood as the action on the equipment, system or installations based on the previous knowledge about the operation condition or performance, obtained by means of parameters previously determined (Bonaldi et al, 2007).

Since the induction motors are the center of the vast majority of the industrial processes, this chapter gives total emphasis to the failure analysis and identification of this kind of electrical machine. Like all the rotating machines, the induction motors are exposed to many different adversities such as thermal and environmental stresses and mechanical damages, which demand maximum attention (Lambert-Torres et al., 2003). Usually, in industries, attention must be even larger since the downtime costs are very high. High and medium voltage induction motors are highly used in industrial processes. Many of them are strategic to the productive process and, because of that, looking for solutions that minimize the failure statistics is mandatory. In most cases, these motors are highly reliable and extremely expensive, forcing the company to operate without a stand-by.

Many predictive techniques are applied to these motors to reduce the number of unplanned outage. The most common techniques applied to fault detection in induction motors are: vibration analysis, acoustical analysis, speed oscillations, partial discharges, circuit analysis, etc. The analyses based on mechanical concepts are established, but the techniques based on electrical signature analysis are being introduced only now. Because of that status, the application of Electrical Signature Analysis (ESA) to industries is the concern of this chapter.

© 2012 Bonaldi et al., licensee InTech. This is an open access chapter 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. © 2012 Bonaldi 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.

The industries currently look for products and outside services for predictive maintenance. In many cases, the outside service company or even the industrial plant predictive group make mistakes that can compromise the whole condition monitoring and failure diagnosis process.

Predictive Maintenance by Electrical Signature Analysis to Induction Motors 489

contributions, the chapter intends to disseminate important concepts to guide companies that have their own predictive group or want to hire consultants or specialized service to obtain good results through general predictive maintenance practices and, especially through electrical signature analysis. For this purpose, the chapter presents a discussion between condition monitoring and troubleshooting, pointing the differences between both

The result of the proposed discussion in this chapter is a procedure of acquisition and analysis, which is presented at the end of the chapter and intends to be a reference to be used by industries that have a plan to have ESA as a monitoring condition tool for electrical

The motors are the center of the majority of the industrial production processes. Therefore, these machines deserve concerns to increase the reliability of the productive process. In this sense, many techniques have been developed for an on-line motor monitoring of the

Monitoring condition of electric machines is an evaluation continuous process of the health of equipment during all its useful life. The main function of a monitoring predictive system is to recognize the development of failures in an initial state. For the maintenance department, each failure must be detected as soon as possible in order to promote a

The process of continuous monitoring of the condition of vital electric machines for the production process brings significant benefits for the company. The main benefits are: bigger efficiency of the productive process, reduction of the losses for not-programmed stops, increase of the useful life of the equipment, and build a historical of failure (Legowski

A continuous monitoring system must observe parameters that give to the maintenance team trustworthy information for the decision-making. The more usual monitored parameters are: voltage and current of the stator; temperature of the nucleus; level of vibration; instantaneous power; level of contamination in the lubricant of the rolling; speed

In such a way, it can be noticed that this area of the technology demands knowledge of the functioning of electric machines, instrumentation, microprocessors, processing of signal,

"Maintenance" can be understood as the action to repair or to execute services in equipment

approaches and the main benefits and problems involved with each one.

**2. Considerations about maintenance** 

behavior and performance.

programmed stop of the machine.

of rotation; flow of escape; and so on.

et al., 1996; Tavner et al., 1997; Thomson & Fenger, 2001).

analysis of materials, chemical analysis, analysis of vibrations, etc.

and systems. It can have its activities classified in four main groups:

**2.1. Classification of the maintenance activities** 

machines.

In this increasing demand for prediction technology, a specific technique referred as Electrical Signature Analysis (ESA) is calling more and more attention of industries. Considering this context, this chapter intends to disseminate important concepts to guide companies that have their own predictive group or want to hire consultants or specialized service to obtain good results through general predictive maintenance practices and, especially through electrical signature analysis.

Figure 1 presents the comparative between vibration analysis and ESA (considering Motor Current Signature Analysis (MCSA), Extended Park's Vector Approach (EPVA) and Instantaneous Power Signature Analysis (IPSA)), showing which technique is more recommended to a specific kind of problem in a determined part of the rotating drive train. One can say that those techniques are complementary.

**Figure 1.** Comparison of predictive maintenance techniques

The main objective of this chapter is to present a procedure to acquire and analyze electrical signals for condition monitoring of electrical machines through motor current signature analysis in order to get the best possible results in an industrial environment. As secondary contributions, the chapter intends to disseminate important concepts to guide companies that have their own predictive group or want to hire consultants or specialized service to obtain good results through general predictive maintenance practices and, especially through electrical signature analysis. For this purpose, the chapter presents a discussion between condition monitoring and troubleshooting, pointing the differences between both approaches and the main benefits and problems involved with each one.

The result of the proposed discussion in this chapter is a procedure of acquisition and analysis, which is presented at the end of the chapter and intends to be a reference to be used by industries that have a plan to have ESA as a monitoring condition tool for electrical machines.
