**10. References**

Benbouzid, M.H. (2000). A Review of Induction Motors Signature Analysis as a Medium for Faults Detection, *IEEE Transactions on Industrial Electronics*, Vol.47, No.5, (October 2000), pp. 984-993, ISSN 0278-0046.

Bonaldi, E.L., Borges da Silva, L.E., Lambert-Torres, G. de Oliviera, L.E.L. (2003). A Rough Sets Based Classifier for Induction Motors Fault Diagnosis, *WSEAS Transactions on Systems*, Vol.2, No.2, (April 2003), pp. 320-327, ISSN 109-2777.

518 Induction Motors – Modelling and Control

**9. Conclusions** 

electrical machines.

**Author details** 

*PS Solutions, Brazil* 

Luiz Eduardo Borges da Silva *Itajuba Federal University, Brazil* 

FAPEMIG for support this work.

pp. 984-993, ISSN 0278-0046.

**Acknowledgement** 

**10. References** 

Considering that 1 day without production means losses of US\$ 300,000.00, we would have US\$ 21,000,000.00 in 70 days. However, as we found a motor to be adapted, we had just 6 days of losses (US\$ 1,800,000.00). If we had an ESA System installed monitoring this motor, we could realize in advance that the motor was developing a failure. As we said before, some refineries have similar motors that could be adapted. So, in that case, it would be possible to plan the replacement, sending the motor, and making the adaptations and

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. 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, the presented 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

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 MCSA as a monitoring condition tool for

The academic authors would like to express their thanks for CNPq, CAPES, FINEP and

Benbouzid, M.H. (2000). A Review of Induction Motors Signature Analysis as a Medium for Faults Detection, *IEEE Transactions on Industrial Electronics*, Vol.47, No.5, (October 2000),

stopping the production for only 1 day, i.e. losses of US\$ 300,000.00.

and, especially through electrical signature analysis.

Erik Leandro Bonaldi, Levy Ely de Lacerda de Oliveira, Jonas Guedes Borges da Silva and Germano Lambert-Torres


Thorsen, O.V. & Dalva, M. (1999). Failure Identification and Analysis for High-Voltage Induction Motors in the Petrochemical Industry, *IEEE Transactions on Industry Applications*, Vol.35, No.4, (July/August 1999), pp. 810-817, ISSN 0093-9994.

**Chapter 21** 

© 2012 Jaksch, 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 Jaksch, 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.

**Rotor Cage Fault Detection in Induction Motors** 

Rotor cage faults as broken rotor bars, increased bars resistance and end-ring faults can be caused by thermal stresses, due to overload, overheating and thus mechanical stresses, magnetic stresses and dynamic stresses due to shaft torques. Environmental stresses as contamination or abrasion also contributes to the rotor cage faults. The rotor cage faults can also lead to the shaft vibration and thus bearing failures and air gap dynamic eccentricity.

Various rotor cage faults detection techniques for induction motors (IM) have been proposed during the last two decades. One of these is a widely used Motor Current Signature Analysis (MCSA) representing namely the direct spectral analysis of stator current (Thomson & Fenger, 2001; Jung et.al, 2006). MCSA can be combined with other methods as stray flux detection and a radial and axial vibration analysis. MCSA is still an open research

Strongly nonstationary working conditions as start-up current analysis require the application of methods generally called Joint Time Frequency Analysis (JTFA). These methods are Short Time Fourier Transform, Continuous Wavelet Transform (Cusido et.al, 2008; Riera-Guasp, 2008), Discrete Wavelet Transform (Kia et. al., 2009), Wigner Distribution (Blödt et al., 2008), etc. The fundamental of the wavelet analysis is the stator current decomposition into a determined number of detailed and approximation components and their pattern recognition. Wavelet

The Vienna monitoring method –VMM (Kral et. al, 2008) is a rotor fault detection method based on instantaneous torque evaluation determined by voltage and current models. Other introduced methods for IM rotor fault diagnostics are multivariable monitoring (Concari et.al, 2008), artificial neural networks and neural network modeling (Su & Chong, 2007), fuzzy based approach (Zidani et. al., 2008), wavelet analysis together with hidden Markov

analysis can be combined with other methods as a torsional vibration (Kia et. al., 2009).

**by Motor Current Demodulation Analysis** 

Additional information is available at the end of the chapter

topic, namely in the region of higher harmonics.

Ivan Jaksch

http://dx.doi.org/10.5772/47811

**1. Introduction** 
