Title: Fault detection of motor's inter-turn short circuit based on BP neural network

Authors: Zhenzhou Wang; Ming Han; Peng Liu; Jiaomin Liu

Addresses: Institute of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, China. ' College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 050021, China. ' Polytechnic Institute, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, China. ' Institute of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, Hebei, 050018, China; College of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei, 050021, China

Abstract: The induced electromotive force signals of air gap were sampled by detection coil's electromotive. Fault mechanism of inter-turn short circuit was analysed. Then we constructed a math model. Performance and parameter characteristics of armature winding's air gap electromotive were analysed by BP neural network algorithm, and applied into inter-turn short circuit's treatment process, and then we constructed the fault detection system of motor's inter-turn short circuit. The system realises fault diagnosis and can find fault slot's location.

Keywords: inter-turn short circuits; detection coil; electromotive force signals; BP neural networks; air gap; mathematical modelling; motor fault diagnosis; armature winding.

DOI: 10.1504/IJMIC.2012.047732

International Journal of Modelling, Identification and Control, 2012 Vol.16 No.3, pp.234 - 240

Published online: 17 Dec 2014 *

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