Research Article
Support Vector Machine-Based Decision for Induction Motor Fault Diagnosis Using Air-Gap Torque Frequency
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 38 - Issue 5 |
| Published: January 2012 |
| Authors: Samira Ben Salem, Khmais Bacha, Abdelkader Chaari |
10.5120/4686-6812
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Samira Ben Salem, Khmais Bacha, Abdelkader Chaari . Support Vector Machine-Based Decision for Induction Motor Fault Diagnosis Using Air-Gap Torque Frequency. International Journal of Computer Applications. 38, 5 (January 2012), 27-33. DOI=10.5120/4686-6812
@article{ 10.5120/4686-6812,
author = { Samira Ben Salem,Khmais Bacha,Abdelkader Chaari },
title = { 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 },
doi = { 10.5120/4686-6812 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Samira Ben Salem
%A Khmais Bacha
%A Abdelkader Chaari
%T Support Vector Machine-Based Decision for Induction Motor Fault Diagnosis Using Air-Gap Torque Frequency%T
%J International Journal of Computer Applications
%V 38
%N 5
%P 27-33
%R 10.5120/4686-6812
%I Foundation of Computer Science (FCS), NY, USA
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
- M.-Y.Chow, "Guest editorial special section on motor fault detection and diagnosis", IEEE Transactions on Industrial Electronics, vol.47, n°5, October 2000, pp.982-983.
- W.Thomson, M.Fenger, "Current signature analysis to detect induction motor faults", IEEE Industry Applications Magazine, July/August 2001, pp.26-34.
- Jawad Faiz, Mansour Ojaghi, "Different indexes for eccentricity faults diagnosis in three-phase squirrel-cage induction motors: A review ", Mechatronics 19 (2009) pp 2–13
- Jee-Hoon Jung, Jong-Jae Lee, and Bong-Hwan Kwon, "Online Diagnosis of Induction Motors Using MCSA" IEEE Transactions On Industrial Electronics, Vol. 53, No. 6, December 2006 pp 1842-1852
- M’hamed Drif and A. J. Marques Cardoso, "Air-gap-Eccentricity Fault Diagnosis, in Three-Phase Induction Motors, by the Complex Apparent Power Signature Analysis", IEEE Transactions On Industrial Electronics, Vol. 55, No. 3, March 2008 pp 1404-1410
- K. Bacha, M. Gossa, G-A. Capolino, Comparative Investigation of Diagnosis Media of Stator Voltage Unbalance and Rotor Broken Bars in Induction Motor. In Proceedings of IEEE Industrial Electronics -IECON’2006 – 32nd annual conference on. 6-10 NOV 2006. pp 5040-5045 Paris (France)
- J.S. Hsu, "Monitoring of defect in induction motors through air-gap torque observation", IEEE Transactions on Industry Applications, vol.31, n°6, september/october 1995, pp.1016-1021.
- K. Bacha, S. Souahlia, M. Gossa, "Power transformer fault diagnosis based on dissolved gas analysis by support vector machine", Electric Power Systems Research 83 (2012) 73-79.
- K. Bacha, H. Henao, M. Gossa, G-A. Capolino, "Induction Machine Fault Detection Using Stray Flux EMF Measurement and Neural Network-Based Decision", Electric Power Systems Research 78 (2008) 1247–1255.
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