期刊
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
卷 20, 期 2, 页码 403-420出版社
ACADEMIC PRESS LTD ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2004.10.010
关键词
Dempster-Shafer theory; data fusion; neural network; fault diagnosis; induction motor; condition monitoring; vibration signal; stator current signal
This paper presents an approach for the fault diagnosis in induction motors by using Dempster-Shafer theory. Features are extracted from motor stator current and vibration signals and with reducing data transfers. The technique makes it possible for on-line application. Neural network is trained and tested by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electrical and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real-time applications. (c) 2004 Elsevier Ltd. All rights reserved.
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