哈尔滨工业大学学报（英文版）2005，Vol.12Issue(3)：266-270,5.
Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks
Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks
LIU Man-lan 1ZHU Chun-bo 2WANG Tie-cheng2
作者信息
- 1. School of Mechanical and Electrical Engineering, Harbin Institute of Technology Harbin 150001, China
- 2. School of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
- 折叠
摘要
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.关键词
DC motor/current analysis/BP neural networks/fault detection/fault diagnosisKey words
DC motor/current analysis/BP neural networks/fault detection/fault diagnosis分类
信息技术与安全科学引用本文复制引用
LIU Man-lan,ZHU Chun-bo,WANG Tie-cheng..Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks[J].哈尔滨工业大学学报（英文版）,2005,12(3):266-270,5.
