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Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks
Authors:LIU Man-lan  ZHU Chun-bo  WANG Tie-cheng
Affiliation:1. School of Mechanical and Electrical Engineering, Harbin Institute of Technology Harbin 150001, China;School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
2. School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
Abstract:In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.
Keywords:DC motor  current analysis  BP neural networks  fault detection  fault diagnosis
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