Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum


Vol. 8, No. 3, pp. 228-238, Jul. 2008
10.6113/JPE.2008.8.3.228


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 Abstract

Many induction motor broken bar diagnosis methods are based on evaluating special components in machine signals spectrums. Current, power, flux, etc are among these signals. Frequencies related to a broken rotor fault are slip dependent, therefore, correct diagnosis of fault - especially when obtrusive frequency components are present - depends on accurate determination of motor velocity and slip. The traditional methods typically require several sensors that should be pre-installed in some cases. This paper presents a diagnosis method based on only a vibration sensor. Motor velocity oscillation due to a broken rotor causes frequency components at twice slip frequency difference around speed frequency in vibration spectrum. Speed frequency and its harmonics as well as twice supply frequency, can easily and accurately be found in a vibration spectrum, therefore the motor slip can be computed. Now components related to rotor fault can be found. It is shown that a trained neural network - as a substitute for an expert person - can easily categorize the existence and the severity of a fault according to the features extracted from the presented method. This method requires no information about the motor internal structure and has been able to diagnose correctly in all the laboratory tests.


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Cite this article

[IEEE Style]

A. Sadoughi, M. Ebrahimi, M. Moallem, S. Sadri, "Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum," Journal of Power Electronics, vol. 8, no. 3, pp. 228-238, 2008. DOI: 10.6113/JPE.2008.8.3.228.

[ACM Style]

Alireza Sadoughi, Mohammad Ebrahimi, Mehdi Moallem, and Saeid Sadri. 2008. Intelligent Diagnosis of Broken Bars in Induction Motors Based on New Features in Vibration Spectrum. Journal of Power Electronics, 8, 3, (2008), 228-238. DOI: 10.6113/JPE.2008.8.3.228.