Multiclass support vector machines for diagnosis of broken rotor bar faults using advanced spectral descriptors

International Review of Electrical Engineering

Volumn 5, Issue 5, 2010, Pages 2095-2105

  Braham, A ^a   Keskes, H ^a   Lachiri, Z ^a,b  

^a UNIVERSITY OF CARTHAGE   (Tunisia)

^b UNIVERSITY OF TUNIS EL MANAR   (Tunisia)

Author keywords

Broken rotor bar; Fault diagnosis; Induction motor; Multiclass support vector machines; Power spectrum density estimation

Indexed keywords

EID: 79956194388     PISSN: 18276660     EISSN: 25332244     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. A. H. Bonnett, G. S. Soukup, Cause and Analysis of Stator and Rotor Failures in Three-Phase Squirrel-Cage Induction Motors, IEEE Transactions on Industry Applications, vol. 28 n. 4, Jully/August 1992, pp. 921-937.
2. S. Nandi, H. A. Toliyat, Condition monitoring and fault diagnosis of electrical machines-a review, The 34th IEEE INDUSTRY APPLICATIONS Conference Annual Meeting ~IAS~, vol.1, 1999, Phoenix.
3. W. T. Thomson, M. Fenger, Current Signature Analysis to Detect Induction Motor Faults", IEEE Industry Applications Magazine, Jully/August 2001, pp. 26-34.
4. N. M. Elkasabgy, A. R. Eastham, G. E. Dawson, Detection of Broken Bars in The Cage Rotor on an Induction Machine, IEEE Transactions on Industry Applications, vol. 28 n. 1, January/February 1992, pp. 165-171.
5. B. Ayhan, H. J. Trussell, Mo-Yuen Chow, Myung-Hyun Song, On the Use of a Lower Sampling Rate for Broken Rotor Bar Detection With DTFT and AR-Based Spectrum Methods, IEEE Transactions on Industrial Electronics, vol.55 n. 3, March 2008, pp. 1421-1434.
6. A. Bellini, A. Yazidi, F. Filippetti, C. Rossi, G. Capolino, High Frequency Resolution Techniques for Rotor Fault Detection of Induction Machines, IEEE Transactions on Industrial Electronics, vol. 55 n. 12, December 2008, pp. 4200-4209.
7. A. Ordaz-Moreno, R. de Jesus Romero-Troncoso, J. A. Vite-Frias, J. R. Rivera-Gillen, A. Garcia-Perez, Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation, IEEE Transactions on Industrial Electronics, vol. 55 n. 5, May 2008, pp. 2193-2202.
8. A. Lebaroud, G. Clerc, Classification of Induction Machine Faults by Optimal Time-Frequency Representations, IEEE Transactions on Industrial Electronics, vol. 55 n. 12, December 2008, pp. 4290-4298.
9. M. Pineda-Sanchez, M. Riera-Guasp, J.A. Antonino-Daviu, J. Roger-Folch, J. Perez-Cruz, R. Puche-Panadero, Instantaneous Frequency of the Left Sideband Harmonic During the Start-Up Transient: A New Method for Diagnosis of Broken Bars, IEEE Transactions on Industrial Electronics, vol. 56 n. 11, November 2009, pp. 4557-4570.
10. W. T. Thomson, M. Fenger, Case histories of current signature analysis to detect faults in induction motor drives, The IEEE International ELECTRIC MACHINES and DRIVES Conference ~IEMDC~, 2003.
11. M. E. H. Benbouzid, A Review of Induction Motors Signature Analysis as a Medium for Faults Detection, IEEE Transactions on Industrial Electronics, vol. 47 n. 5, October 2000, pp. 984-993.
12. J. Cusidó, L. Romeral, J. A. Ortega, A. Garcia, J. R. Riba, Wavelet and PDD as Fault Detection Techniques, Electric Power Systems Research, vol. 80, 2010, pp. 915-924.
13. M. H. Hayes, Statistical Digital Signal Processing and Modeling (Wiley, 1996).
14. L. A. Pereira, D. Fernandas, D. S. Gazzana, F.B. Libano, S. Haffner, Performance evaluation of nonparametric, parametric, and the MUSIC methods to detection of rotor Cage Faults of induction motors, The 32nd Annual Conference on IEEE INDUSTRIAL ELECTRONICS ~IECON~, November 6-10, 2006, Paris, France.
15. G. Niu, A. Widodo, J. D. Son, B. S. Yang, D. H. Hwang, D. S. Kang, Decision-level Fusion Based on Wavelet Decomposition for Induction Motor Fault Diagnosis Using Transient Current Signal, Expert Systems with Applications, vol. 35, October 2008, pp. 918-928.
16. L. Eren, Y. Çekiç, M. J. Devaney, Broken Rotor Bar Detection via Wavelet Packet Decomposition of Motor Current, International Review of Electrical Engineering (IREE), vol. 4 n. 5, October 2009, pp. 844-850.
17. O. Ondel, E. Boutleux, G. Clerc, A method to detect broken bars in induction machine using pattern recognition techniques, IEEE Transactions on Industry Applications, vol. 42 n. 4, July/August 2006, pp. 916-923.
18. A. Felkaoui, R. E. Bekka, J. Antoni, M. Sidahmed, S. Fedala, Bearing Fault Diagnosis of an Induction Motor: Feature Selection Importance in Pattern Recognition Analysis, International Review of Electrical Engineering (IREE), vol. 2 n. 1, August 2007, pp. 587-592.
19. F. Filippetti, G. Franceschini, C. Tassoni, P. Vas, Recent Developments of Induction Motor Drives Fault Diagnosis Using AI Techniques," IEEE Transactions on Industrial Electronics, vol. 47 n. 5, October 2000, pp. 994-1003.
20. H. Razik, M.B. de Rossiter Correa, E.R.C. da Silva, A Novel Monitoring of Load Level and Broken Bar Fault Severity Applied to Squirrel-Cage Induction Motors Using a Genetic Algorithm, IEEE Transactions on Industrial Electronics, vol. 56 n. 11, November 2009, pp. 4615-4626.
21. H. Su, K. T. Chong, Induction Machine Condition Monitoring Using Neural Network Modeling," IEEE Transactions on Industrial Electronics, vol. 54 n. 1, February 2007, pp. 241-249.
22. B. Yang, T. Han, W. Hwang, Fault Diagnosis of Rotating Machinery Based on Multi-Class Support Vector Machines, Journal of Mechanical Science and Technology, vol. 19 n. 3, 2005, pp 846-859.
23. A. Widodo, B. Yang, T. Han, Combination of Independent Component Analysis and Support Vector Machines for Intelligent Faults Diagnosis of Induction Motors, Expert Systems with Applications, vol. 32, 2007, pp. 299-312.
24. Ge Ming, Xu Yangsheng, Du Ruxu, An Intelligent Online Monitoring and Diagnostic System for Manufacturing Automation, IEEE Transactions on Automation Science and Engineering, vol. 5 n. 1, January 2008, pp. 127-139.
25. J. Kurek, S. Osowski, Support Vector Machine for Fault Diagnosis of the Broken Rotor Bars of Squirrel-Cage Inductance Motor, Neural Computing & Applications, October 2009.
26. M. Mohammadi, G. B. Gharehpetian, Application of Multi-Class Support Vector Machines for Power System On-Line Static Security Assessment, International Review of Electrical Engineering (IREE), vol. 3 n. 3, June 2008, pp. 532-542.
27. V. Vapnik, Statistical Learning Theory (New York: Wiley, 1998).
28. P. Stoica, R. L. Moses, Spectral Analysis of Signals (Prentice Hall, 2005).
29. S. H. Kia, H. Henao, G.-A. Capolino, Digital Signal Processing for Induction Machines Diagnosis - A Review, The 33rd Annual Conference of the IEEE INDUSTRIAL ELECTRONICS SOCIETy ~IECON~, November 5-8, 2007, Taipei, Taiwan.
30. F. Castanié, Spectral Analysis: Parametric and Non-Parametric Digital Methods (ISTE Ltd, 2006).
31. C. Hsu and C. Lin, A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions on Neural Networks, vol. 13 n. 2, March 2002, pp. 415-425.
32. R. Rifkin and A. Klautau, In defense of One-Vs-All Classification, Journal of Machine Learning Research, vol. 5, January 2004, pp. 101-141.
33. A. Braham, Z. Lachiri, Diagnosis of Broken Bar Fault in Induction Machines Using Advanced Digital Signal Processing, International Review of Electrical Engineering (IREE),), vol. 5 n. 4, August 2010, pp. 1460-1468.
34. V. Franc, V. Hlava, Statistical Pattern Recognition Toolbox for Matlab, Czech Technical University, June 2004, Available from http://cmp.felk.cvut.cz/cmp/software/stprtool
35. J. C. Platt, Fast training of support vector machines using sequential minimal optimization, in Cambridge (Ed.), C. J. C. Burges, A. J. Smola, B. Schölkopf, Advances in Kernel Methods-Support Vector Learning, (MA: MIT Press, 1998).