NDLI: An unsupervised, on-line system for induction motor fault detection using stator current monitoring

Content Provider	IEEE Xplore Digital Library
Author	Schoen, R.R. Lin, B.K. Habetler, T.G. Schlag, J.H. Farag, S.
Copyright Year	1972
Abstract	A new method for online induction motor fault detection is presented in this paper. This system utilizes artificial neural networks to learn the spectral characteristics of a good motor operating online. This learned spectrum may contain many harmonics due to the load which correspond to normal operating conditions. In order to reduce the number of harmonics which are continuously monitored to a manageable number, a selective frequency filter is employed. This frequency filter only passes those harmonics which are known to be of importance in fault detection, or which are continuously above a set level, to a neural net clustering algorithm. After a sufficient training period, the neural network signals a potential failure condition when a new cluster is formed and persists for some time. Since a fault condition is found by comparison to a prior condition of the machine, online failure prediction is possible with this system without requiring information on the motor or load characteristics. The detection algorithm was implemented and its performance verified on various fault types.
Sponsorship	IEEE Industry Applications Society
Starting Page	1280
Ending Page	1286
Page Count	7
File Size	890708
File Format	PDF
ISSN	00939994
Volume Number	31
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Induction motors Fault detection Stators Condition monitoring Neural networks Expert systems Power harmonic filters Frequency Rotors Induction machines
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Electrical and Electronic Engineering

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