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Support Vector Machine-based Decision for Induction Motor Fault Diagnosis using Air-Gap Torque Frequency Response

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 38 - Number 5
Year of Publication: 2012
Authors:
Samira Ben Salem
Khmais Bacha
Abdelkader Chaari
10.5120/4686-6812

Samira Ben Salem, Khmais Bacha and Abdelkader Chaari. Article: Support Vector Machine-Based Decision for Induction Motor Fault Diagnosis Using Air-Gap Torque Frequency. International Journal of Computer Applications 38(5):27-33, January 2012. Full text available. BibTeX

@article{key:article,
	author = {Samira Ben Salem and Khmais Bacha and Abdelkader Chaari},
	title = {Article: Support Vector Machine-Based Decision for Induction Motor Fault Diagnosis Using Air-Gap Torque Frequency},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {38},
	number = {5},
	pages = {27-33},
	month = {January},
	note = {Full text available}
}

Abstract

In this work we propose the air-gap torque as failure signature to detect mechanical faults in particular the eccentricity. In this way, we compare the proposed signature with those most used recently in particular the current space vector (Park vector) and complex apparent power. This signature is subsequently analysed using the classical fast Fourier transform (FFT). The magnitudes of spectral components relative to the studied fault are extracted in order to develop the input vector necessary for the pattern recognition tool based on support vector machine (SVM) approach with an aim of classifying automatically the various states of the induction motor.

References

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