Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Han, Tian; Yang, Bo-Suk; Choi, Won-Ho; Kim, Jae-Sik

doi:https://doi.org/10.1155/IJRM/2006/61690

International Journal of Rotating Machinery

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Volume 2006 | Article ID 061690 | https://doi.org/10.1155/IJRM/2006/61690

Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

Tian Han,¹Bo-Suk Yang,²Won-Ho Choi,³and Jae-Sik Kim⁴

Received23 Jan 2006

Revised22 May 2006

Accepted04 Jun 2006

Published17 Aug 2006

Abstract

This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT), feature extraction, genetic algorithm (GA), and neural network (ANN) techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

References

A. H. Bonnett, “Root cause AC motor failure analysis with a focus on shaft failures,” IEEE Transactions on Industry Applications, vol. 36, no. 5, pp. 1435–1448, 2000.
View at: Publisher Site | Google Scholar
G. K. Singh and S. A. S. Al Kazzaz, “Induction machine drive condition monitoring and diagnostic research - a survey,” Electric Power Systems Research, vol. 64, no. 2, pp. 145–158, 2003.
View at: Publisher Site | Google Scholar
W. T. Thomson and M. Fenger, “Current signature analysis to detect induction motor faults,” IEEE Industry Applications Magazine, vol. 7, no. 4, pp. 26–34, 2001.
View at: Publisher Site | Google Scholar
W. T. Thomson, D. Rankin, and D. G. Dorrell, “On-line current monitoring to diagnose airgap eccentricity in large three-phase induction motors—industrial case histories verify the predictions,” IEEE Transactions on Energy Conversion, vol. 14, no. 4, pp. 1372–1378, 1999.
View at: Publisher Site | Google Scholar
S. Williamson and K. Mirzoian, “Analysis of cage induction motors with stator winding faults,” IEEE Transactions on Power Apparatus and Systems, vol. 104, no. 7, pp. 1838–1842, 1985.
View at: Google Scholar
R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartfield, “Motor bearing damage detection using stator current monitoring,” IEEE Transactions on Industry Applications, vol. 31, no. 6, pp. 1274–1279, 1995.
View at: Publisher Site | Google Scholar
F. Thollon, G. Grellet, and A. Jammal, “Asynchronous motor cage fault detection through electromagnetic torque measurement,” European Transactions on Electrical Power Engineering, vol. 3, no. 5, pp. 375–378, 1993.
View at: Google Scholar
E. R. Filho, R. R. Riehl, and E. Avolio, “Automatic three-phase squirrel-cage induction motor test assembly for motor thermal behavior studies,” in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE '94), pp. 204–209, Santiago, Chile, May 1994.
View at: Google Scholar
R. Beguenane and M. E. H. Benbouzid, “Induction motors thermal monitoring by means of rotor resistance identification,” IEEE Transactions on Energy Conversion, vol. 14, no. 3, pp. 566–570, 1999.
View at: Publisher Site | Google Scholar
Y. S. Lee, J. K. Nelson, H. A. Scarton, D. Teng, and S. Azizi-Ghannad, “An acoustic diagnostic technique for use with electric machine insulation,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 1, no. 6, pp. 1186–1193, 1994.
View at: Publisher Site | Google Scholar
G. C. Stone, H. G. Sedding, and M. J. Costello, “Application of partial discharge testing to motor and generator stator winding maintenance,” IEEE Transactions on Industry Applications, vol. 32, no. 2, pp. 459–464, 1996.
View at: Publisher Site | Google Scholar
G. C. Stone, “The use of partial discharge measurements to assess the condition of rotating machine insulation,” IEEE Electrical Insulation Magazine, vol. 12, no. 4, pp. 23–27, 1996.
View at: Google Scholar
F. Filippetti, G. Franceschini, C. Tassoni, and P. Vas, “Recent developments of induction motor drives fault diagnosis using AI techniques,” IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp. 994–1004, 2000.
View at: Publisher Site | Google Scholar
A. C. McCormick and A. K. Nandi, “Classification of the rotating machine condition using artificial neural networks,” Proceedings of the Institution of Mechanical Engineers, Part C, vol. 211, no. 6, pp. 439–450, 1997.
View at: Google Scholar
D. C. Baillie and J. Mathew, “A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings,” Mechanical Systems and Signal Processing, vol. 10, no. 1, pp. 1–17, 1996.
View at: Publisher Site | Google Scholar
B. Samanta and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling element bearings using time-domain features,” Mechanical Systems and Signal Processing, vol. 17, no. 2, pp. 317–328, 2003.
View at: Publisher Site | Google Scholar
T. Bi, Z. Yan, F. Wen et al., “On-line fault section estimation in power systems with radial basis function neural network,” International Journal of Electrical Power and Energy Systems, vol. 24, no. 4, pp. 321–328, 2002.
View at: Publisher Site | Google Scholar
N. S. Vyas and D. Satishkumar, “Artificial neural network design for fault identification in a rotor-bearing system,” Mechanism and Machine Theory, vol. 36, no. 2, pp. 157–175, 2001.
View at: Google Scholar
C. T. Kowalski and T. Orlowska-Kowalska, “Neural networks application for induction motor faults diagnosis,” Mathematics and Computers in Simulation, vol. 63, no. 3–5, pp. 435–448, 2003.
View at: Publisher Site | Google Scholar
G. A. Carpenter and S. Grossberg, “The art of adaptive pattern recognition by a self-organizing neural network,” Computer, vol. 21, no. 3, pp. 77–88, 1988.
View at: Publisher Site | Google Scholar
E. Llobet, E. L. Hines, J. W. Gardner, P. N. Bartlett, and T. T. Mottram, “Fuzzy ARTMAP based electronic nose data analysis,” Sensors and Actuators, B: Chemical, vol. 61, no. 1, pp. 183–190, 1999.
View at: Publisher Site | Google Scholar
G. A. Carpenter and S. Grossberg, “ART2: self-organization of stable category recognition codes for analog input patterns,” Applied Optics, vol. 26, no. 23, pp. 217–231, 1987.
View at: Google Scholar
G. A. Carpenter and S. Grossberg, “ART 3. Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures,” Neural Networks, vol. 3, no. 2, pp. 129–152, 1990.
View at: Google Scholar
G. A. Carpenter and S. Grossberg, “A self-organizing neural network,” IEEE Communications Magazine, vol. 30, no. 9, pp. 38–49, 1992.
View at: Google Scholar
B.-S. Yang, T. Han, and J. L. An, “ART-KOHONEN neural network for fault diagnosis of rotating machinery,” Mechanical Systems and Signal Processing, vol. 18, no. 3, pp. 645–657, 2004.
View at: Publisher Site | Google Scholar
C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995.
T. Han, J. L. An, and B.-S. Yang, “Feature extraction of vibration signal for machine condition monitoring,” in Proceedings of 3rd Asia-Pacific Conference on Systems Integrity and Maintenance (ACSIM '02), pp. 126–134, Cairns, Australia, September 2002.
View at: Google Scholar
A. Neumaier and T. Schneider, “Estimation of parameters and eigenmodes of multivariate autoregressive models,” ACM Transactions on Mathematical Software, vol. 27, no. 1, pp. 27–57, 2001.
View at: Publisher Site | Google Scholar
R. B. Randall, Frequency Analysis, Brüel & Kjær, Naerum, Denmark, 1987.
W. A. Gardner, “Measurement of spectral correlation,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 5, pp. 1111–1123, 1986.
View at: Google Scholar
G. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison Wesley, New York, NY, USA, 1989.
M. Misiti, Y. Misiti, G. Oppenheim, and J. M. Poggi, Wavelet Toolbox for Use with MATLAB, The Math Works, Natick, Mass, USA, 1996.
I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, Pa, USA, 1992.

Copyright

Copyright © 2006 Tian Han et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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