Online fault detection of induction motors using frequency domain independent components analysis

Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics

Volumn , Issue , 2011, Pages 2132-2137

  Wang, Z ^a   Chang, C S ^b  

^a INSTITUTE OF HIGH PERFORMANCE COMPUTING   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Fast Fourier Transform (FFT); Fault detection; Features of the frequency signatures (FS features); Independent Component Analysis (ICA); Induction Motor

Indexed keywords

FAULT SIGNATURE; FEATURES OF THE FREQUENCY SIGNATURES (FS FEATURES); FREQUENCY DOMAINS; INDEPENDENT COMPONENTS; MEASUREMENT NOISE; MOTOR PARAMETERS; ON-LINE FAULT DETECTION; STATOR CURRENTS; TIME-DOMAIN WAVEFORMS;

FAST FOURIER TRANSFORMS; FAULT DETECTION; INDEPENDENT COMPONENT ANALYSIS; INDUCTION MOTORS; INDUSTRIAL ELECTRONICS; MULTIVARIANT ANALYSIS; TIME DOMAIN ANALYSIS;

FREQUENCY DOMAIN ANALYSIS;

EID: 80052841055     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIE.2011.5984490     Document Type: Conference Paper

References (25)

1. K. Bimal, "Power Electronics and Motor Drives Recent Progress and Perspective," IEEE Transactions on Industrial Electronics, vol. 56, no. 2, pp. 581-588, 2009.
2. M. Khan and M. Rahman, "Development and implementation of a novel fault diagnostic and protection technique for IPM motor drives," IEEE Transactions on Industrial Electronics, vol. 56, no. 1, pp. 85-92, 2009.
3. F. Zidani, D. Diallo, M. El Hachemi Benbouzid, and R. Nait-Said, "A fuzzy-based approach for the diagnosis of fault modes in a voltage-fed PWM inverter induction motor drive," IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 586-593, 2008.
4. O. Alejandro, R. de Jesus, V. Alberto, R. Rooney, and G. Arturo, "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, no. 5, pp. 2193-2202, 2008.
5. Z. Wang, C. S. Chang, X. German, and W. W. Tan, "Online Fault Detection of Induction Motors Using Independent Component Analysis and Fuzzy Neural Network," in 8th IET International Conference on Advances in Power System Control, Operation and Management, Hong Kong, 2009.
6. Z. Wang, C. S. Chang, T. Chua, and W. W. Tan, "Ensemble and Individual Noise Reduction Method for Induction-motor Signature Analysis," in 8th IET International Conference on Advances in Power System Control, Operation and Management, Hong Kong, 2009.
7. T. Chua, W.W. Tan, Z.Wang, and C. S. Chang, "Hybrid Time-Frequency Domain Analysis for Inverter-Fed Induction Motor Fault Detection," in IEEE International Symposium on Industrial Electronics, ISIE, 2010.
8. S. Nandi, R. Bharadwaj, and H. Toliyat, "Performance analysis of a three-phase induction motor under mixed eccentricity condition," IEEE Transaction on Energy Conversion, vol. 17, no. 3, pp. 392-399, 2002.
9. M. Benbouzid, "Review of induction motors signature analysis as a medium for faults detection," IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 984-993, 2000.
10. G. Kliman, R. Koegl, J. Stein, R. Endicott, and M. Madden, "Noninvasive detection of broken rotor bars in operating inductionmotors," IEEE Transaction on Energy Conversion, vol. 3, no. 4, pp. 873-879, 1988.
11. B. Yazici and G. Kliman, "An adaptive statistical time-frequency method for detection ofbroken bars and bearing faults in motors using stator current," IEEE Transactions on Industry Applications, vol. 35, no. 2, pp. 442-452, 1999.
12. W. Thomson and M. Fenger, "Current signature analysis to detect induction motor faults," IEEE Industry Applications Magazine, vol. 7, no. 4, pp. 26-34, 2001.
13. A. Bellini, F. Filippetti, G. Franceschini, C. Tassoni, and G. Kliman, "Quantitative evaluation of induction motor broken bars by means ofelectrical signature analysis," IEEE Transactions on Industry Applications, vol. 37, no. 5, pp. 1248-1255, 2001.
14. C. Cunha, R. Lyra, and B. Filho, "Simulation and analysis of induction machines with rotor asymmetries," IEEE Transactions on Industry Applications, vol. 41, no. 1, pp. 18-24, 2005.
15. R. Schoen and T. Habetler, "Evaluation and implementation of a system to eliminate arbitraryload effects in current-based monitoring of induction machines," IEEE Transactions on Industry Applications, vol. 33, no. 6, pp. 1571-1577, 1997.
16. B. Ayhan, M. Chow, and M. Song, "Multiple signature processing-based fault detection schemes for broken rotor bar in induction motors," IEEE Transactions on Energy Conversion, vol. 20, no. 2, p. 336, 2005.
17. H. Su and K. Chong, "Induction machine condition monitoring using neural network modeling," IEEE Transactions on Industrial Electronics, vol. 54, no. 1, pp. 241-249, 2007.
18. B. Ayhan, M. Chow, and M. Song, "Multiple discriminant analysis and neural-network-based monolith and partition fault-detection schemes for broken rotor bar in induction motors," IEEE Transactions on Industrial Electronics, vol. 53, no. 4, p. 1298, 2006.
19. B. Akin, U. Orguner, H. Toliyat, and M. Rayner, "Low order PWM inverter harmonics contributions to the inverter-fed induction machine fault diagnosis," IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp. 610-619, 2008.
20. M. Benbouzid and G. Kliman, "What stator current processing-based technique to use for induction motor rotor faults diagnosis?" IEEE Transaction on Energy Conversion, vol. 18, no. 2, pp. 238-244, 2003.
21. J. Jung, J. Lee, and B. Kwon, "Online diagnosis of induction motors using MCSA," IEEE Transactions on Industrial Electronics, vol. 53, no. 6, pp. 1842-1852, 2006.
22. A. A. Amanatiadis and I. Andreadis, "Digital image stabilization by independent component analysis," IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 7, pp. 1755 - 1763, 2010.
23. M. Crespo-Garcia, M. Atienza, and J. Cantero, "Muscle artifact removal from human sleep EEG by using independent component analysis," Annals of Biomedical Engineering, vol. 36, no. 3, pp. 467-475, 2008.
24. C. S. Chang, J. Jin, C. Chang, T. Hoshino, M. Hanai, and N. Kobayashi, "Online source recognition of partial discharge for gas insulated substations using independent component analysis," IEEE Transactions on Dielectrics and Electrical Insulation, vol. 13, no. 4, p. 892, 2006.
25. D. Tsai and S. Lai, "Independent component analysis-based background subtraction for indoor surveillance," IEEE Transactions on Image Processing, vol. 18, no. 1, pp. 158-167, 2009.