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Real time application of artificial neural network for incipient fault detection of induction machines

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Sui Oi YeeAuthors Info & Claims

IEA/AIE '90: Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

Pages 1030 - 1036

https://doi.org/10.1145/98894.99117

Published: 01 June 1990 Publication History

Abstract

This paper describes several artificial neural network architectures for real time application in incipient fault detection of induction machines. The artificial neural networks perform the fault detection in real time, based on direct measurements from the motor, and no rigorous mathematical model of the motor is needed. Different approaches used to develop a reliable fault detector are presented and compared in this paper. The designed networks vary in complexity and accuracy. A high-order fault detector neural network is discussed first. Then noise considerations are included in more complex fault detector models, since noise is an important factor in the design and analysis of real time fault detector neural networks. Simulation results show that with appropriate designs, artificial neural networks perform satisfactorily in real time incipient fault detection of induction machines.

References

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Mo-yuen Chow, Sui Oi Yee, "Application of Neural Networks to Incipient Fault Detection in induction Motors," to be published in Journal of Neural Network Computing, Vol. 2, No. 2, January 1990.

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Mo-yuen Chow, Sui Oi Yee, Electric Power Research Center- NCSU 1989 Annual Report E-5- 31067, January 1990.

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Digital Library

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Max Reid, et.al., "Rapid Training of Higher-Order Neural Networks for Invariant Pattern Recognition," IJCNN, Washington, D.C., 1989.

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Mo-yuen Chow, Robert J. Thomas, "Neural Network Synchronous Machine Modeling," 1989 International Symposium on Circuits and Systems, Portland, Oregon, May 9-11, 1989.

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Cited By

Leite DHell MCosta PGomide F(2009)Real-time fault diagnosis of nonlinear systemsNonlinear Analysis: Theory, Methods & Applications10.1016/j.na.2009.06.03771:12(e2665-e2673)Online publication date: Dec-2009
https://doi.org/10.1016/j.na.2009.06.037
Knapp GJavadpour RWang H(2000)An ARTMAP neural network‐based machine condition monitoring systemJournal of Quality in Maintenance Engineering10.1108/135525100103280956:2(86-105)Online publication date: Jun-2000
https://doi.org/10.1108/13552510010328095
Bo Li Goddu GMo-Yuen Chow (1998)Enhancement of the performance of a neural network based motor fault detector using graphical data analysis techniques1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)10.1109/IJCNN.1998.682237(63-68)Online publication date: 1998
https://doi.org/10.1109/IJCNN.1998.682237
Show More Cited By

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Real time application of artificial neural network for incipient fault detection of induction machines

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cover image ACM Conferences

IEA/AIE '90: Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2

June 1990

591 pages

ISBN:0897913728

DOI:10.1145/98894

Chairman:
Manton M. Matthews

Copyright © 1990 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 June 1990

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IEA/AEI-90: 3rd International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems

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Cited By

Leite DHell MCosta PGomide F(2009)Real-time fault diagnosis of nonlinear systemsNonlinear Analysis: Theory, Methods & Applications10.1016/j.na.2009.06.03771:12(e2665-e2673)Online publication date: Dec-2009
https://doi.org/10.1016/j.na.2009.06.037
Knapp GJavadpour RWang H(2000)An ARTMAP neural network‐based machine condition monitoring systemJournal of Quality in Maintenance Engineering10.1108/135525100103280956:2(86-105)Online publication date: Jun-2000
https://doi.org/10.1108/13552510010328095
Bo Li Goddu GMo-Yuen Chow (1998)Enhancement of the performance of a neural network based motor fault detector using graphical data analysis techniques1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)10.1109/IJCNN.1998.682237(63-68)Online publication date: 1998
https://doi.org/10.1109/IJCNN.1998.682237
Bo Li Goddu GMo-Yuen Chow (1997)Knowledge based technique to enhance the performance of neural network based motor fault detectorsProceedings of the IECON'97 23rd International Conference on Industrial Electronics, Control, and Instrumentation (Cat. No.97CH36066)10.1109/IECON.1997.668441(1113-1118)Online publication date: 1997
https://doi.org/10.1109/IECON.1997.668441
Chow MSharpe RHung J(1993)On the application and design of artificial neural networks for motor fault detection. IIIEEE Transactions on Industrial Electronics10.1109/41.22264040:2(189-196)Online publication date: Apr-1993
https://doi.org/10.1109/41.222640
Yee SChow M(1991)Robustness of an induction motor incipient fault detector neural network subject to small input perturbationsIEEE Proceedings of the SOUTHEASTCON '9110.1109/SECON.1991.147774(365-369)Online publication date: 1991
https://doi.org/10.1109/SECON.1991.147774
Chow MYee S(1991)Robustness test of an incipient fault detector artificial neural networkIJCNN-91-Seattle International Joint Conference on Neural Networks10.1109/IJCNN.1991.155152(73-78)Online publication date: 1991
https://doi.org/10.1109/IJCNN.1991.155152
Chow MYee S(1991)Methodology for on-line incipient fault detection in single-phase squirrel-cage induction motors using artificial neural networksIEEE Transactions on Energy Conversion10.1109/60.843326:3(536-545)Online publication date: Jan-1991
https://doi.org/10.1109/60.84332

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