CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network

by Pu Shi, Zheng Chen, Yuriy Vagapov
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 14
Year of Publication: 2013
Authors: Pu Shi, Zheng Chen, Yuriy Vagapov
10.5120/11914-8034

Pu Shi, Zheng Chen, Yuriy Vagapov . Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network. International Journal of Computer Applications. 69, 14 ( May 2013), 44-50. DOI=10.5120/11914-8034

@article{ 10.5120/11914-8034,
author = { Pu Shi, Zheng Chen, Yuriy Vagapov },
title = { Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 14 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number14/11914-8034/ },
doi = { 10.5120/11914-8034 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:18.481214+05:30
%A Pu Shi
%A Zheng Chen
%A Yuriy Vagapov
%T Fault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 14
%P 44-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a prototype wavelet and probabilistic based neural network classifier for recognizing rotor bar defects is implemented and tested under various transient signals. The wavelet transform (WT) technique is integrated with the neural network model to extract rotor fault features. Firstly, the multiresolution analysis technique of WT and the particle swarm optimization (PSO) theorem are employed to extract the features of the distorted signal. Then, the probabilistic neural network (PNN) classifies these extracted features to identify the rotor defects type. The proposed approach can reduce a great quantity of the distorted signal features without losing its original property. Moreover, less memory space and computing time are required. Various experimental cases tested results show that the hybrid classifier can detect and classify broken rotor bar faults efficiently.

References
  1. Nandi, S and Toliyat, H, Fault diagnosis of electrical machine-a review, IEEE IEMCD'99,International Electric Machines and Drives Conference, May 9–12,Washington, USA, 1999, 219-221.
  2. Kliman, G B, et al. , Noninvasive detection of broken rotor bars in operating induction motors, IEEE Transactions on Energy Conversion, EC-3(4), 1988, 873-879.
  3. Cameron, J R, Thomson, W T and Dow, A B, Vibration and current monitoring for detecting airgap eccentricity in large induction motors, IEEE Proceedings, 133(pt. B, 3), 1986, 155-163.
  4. Schoen, R, et al. , An unsupervised, on-line system for induction motor fault detection using stator current monitoring, IEEE Transactions on Industry Applications, 31(6), 1995, 1280-1286.
  5. Cruz, S. M. C and Cardoso, A. J. M, Stator winding fault diagnosis in three-phase synchronous and asynchronous motors by the extened park's vector approach, Conference Record of the 2000 IEEE Industry Applications Conference, CD-Rom, Roma, Italy, October 2000, 7pp.
  6. Nejjari, H and Benbouzid, M, Monitoring and diagnosis of induction motors electrical faults using a current park's vector pattern learning approach, IEEE Transaction on Industry Applications, 36(3), 2000, 730-735.
  7. Salles, G, et al. , Monitoring of induction motor load by neural network techniques, IEEE Transactions on Power Electronics, 15(4), 2000, 762-768.
  8. Chen, D S and Jain, R C, "A robust back propagation learning algorithm for function approximation," IEEE Trans. Neural Networks, vol. 5, no. 3, pp. 467-479, 1994.
  9. Zhang, Q and Benveniste, A, "Wavelet networks," IEEE Trans. Neural Networks, vol. 3, pp. 889-898, 1992.
  10. Pati, Y C and Krishnaprasad, P S, "Analysis and synthesis of feedforward neural networks using affine wavelet," IEEE Trans. Neural Neworks, vol. 4, no. 1, pp. 73-75, 1993.
  11. Zhang, J, et al. , "Wavelet neural networks for function learning," IEEE Trans. Signal Process, vol. 43, pp. 1485-1497, 1995.
  12. Delyon, B, Juditsky, A and Benveniste, A, "Accuracy analysis for wavelet approximations," IEEE Trans. Neural Networks, vol. 6, pp. 332-348, 1995.
  13. Daubechies, I, "The wavelet transform, time-frequency localization, and signal anaylsis," IEEE Trans. Inform Theory, vol. 36, pp. 961-1005, 1990.
  14. Guo, Q J, Yu, H B and Xu, A D, "A Hybrid PSO-GD Based Intelligent Method for Machine Diagnosis," Digital Signal Processing, vol. 16, pp. 402-418, 2006.
  15. Kennedy, J and Eberhart, R C, "Particle swarm optimization," in: Proceedings of IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
  16. Kennedy, J, Eberhart, R C and Shi, Y, "Swarm Intelligence," Morgan Kaufmann, 2001.
  17. Abido, M A, "Optimal design of power system stabilizers using particle swarm optimization," IEEE Trans. Energy Convers, vol. 17, no. 3, pp. 406-413, 2002.
  18. Parsopoulos, K E and Vrahatis, M N, "Recent approaches to global optimization problems through particle swarm optimization," Natural Comput, vol. 1, pp. 235-306, 2002.
  19. Specht, D F, "Probabilistic neural networks," Neural Networks, vol. 3, no. 1, pp. 109-118, 1990.
  20. Masters, T, "Advanced algorithms for neural networks," a C++ sourcebook, New York : Wiley, 1995.
  21. Parzen, E, "On estimation of a probability density function and mode," The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065-1076, 1962.
  22. Cacoullos, T, "Estimation of a multivariate density," Annals of the Institute of Statistical Mathematics, vol. 18, pp. 179-189, 1966.
  23. Chow, M Y, Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection, s. l. : World Scientific Publishing Co. Pte. Ltd,Singapore, 1997.
  24. Patton, R J and Chen, J, "On-line residual compensation in robust fault diagnosis of dynamic systems," in IFAC Symp Artificial Intelligence in Real-time Control, Delft, The Netherlands, no. 17, pp. 221-227, 1992.
  25. Bertenshaw, D R, et al. , "Detection of stator core faults in large electrical machines," IET Electric Power Applications, vol. 6, no. 6, pp. 295-301, 2012.
  26. Verma, S P and Natarajan, R, "Effects of eccentricity in induction motors," in Proc. Int. Conf. Electrical Machines,Budapest,Hungary, pp. 930-933, 1982.
  27. Walliser, R F and Landy, C F, "Determination of interbar current effects in the detection of broken rotor bars in squirrel cage induction motors," IEEE Trans. Energy Conversion, vol. 9, no. 1, pp. 152-158, 1994.
  28. Morita, I, "Air-gap flux analysis for cage rotor diagnosis," Elec. Eng. In Japan, vol. 112, no. 3, pp. 171-181, 1992.
  29. Ellison, A J and Yang, S J, "Effects of rotor eccentricity on acoustic noise from induction machines," Proc. Inst. Electr. Eng, vol. 118, no. 1, pp. 174-184, 1974.
  30. Toliyat, H A, Lipo, T A and White, J C, "Analysis of a concentrated winding induction machine for adjustable speed drive application part 1 (Motor analysis)," IEEE Transaction on Energy Conversion, vol. 6, no. 4, pp. 679-684, 1991.
  31. Toliyat, H A and Lipo, T A, "Transsient analysis of cage induction machines under stator rotor bar and end-ring faults," IEEE Transaction on Energy Conversion, vol. 10, no. 2, pp. 241-247, 1995.
  32. Ah-jaco, A, "Modélisation des moteurs asynchrones triphasés en régime Transitoire avec saturation et harmoniques d'espace. Application au diagnostic," PhD thesis, Université de Lyon, juillet 1997.
  33. Munoz, A R and Lipo, T A, "Complex vector model of the squirrel-cage induction machine including instantaneous rotor bar currents," IEEE-IAP, vol. 35, no. 6, 1999.
  34. Lipo, T A and Toliyat, H A, "Feasibility study of a converter optimized induction motor," Palo Alto,CA,Electric Power Research Institute,EPRI Final Rep, pp. 2624-02, 1989.
  35. Luo, X, et al. , "Multiple coupled circuit modeling of induction machines," IEEE Transaction on Industry apllications, vol. 31, no. 2, pp. 311-318, March/April 1995.
  36. AI-Nuaim, N A and Toliyat, H A, "A novel method for modeling dynamic air-gap eccentricity in synchronous machines based on modified winding function theory," IEEE Transaction on Energy Conversion, vol. 13, no. 2, pp. 156-162, June 1998.
  37. Houdouin, G, et al. , "Coupled Magnetic Circuit Modeling of the Stator Windings Faults of Induction Machines Including Saturation Effect," In proceedings of the IEEE International Conference on industrial Technology(ICIT), pp. 148-153, 2004
Index Terms

Computer Science
Information Sciences

Keywords

Induction machine WT PSO PNN Broken rotor bar

Powered by PhDFocusTM