期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 68, 期 2, 页码 310-324出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2018.2847800
关键词
Convolution neural network (CNN); deep learning; fault diagnosis; motor current signature analysis; permanent magnet synchronous motor (PMSM); wavelet package transform
This paper presents an effective diagnosis algorithm for permanent magnet synchronous motors running with an array of faults of varying severity over a wide speed range. The fault diagnosis is based on a current signature analysis. The complete fault motor diagnosis system requires the extraction of features based on the current method and a subsequentmethod for adding classifications. In this paper, we propose two feature extraction methods: the first involves a classification method that utilizes a wavelet packet transform and the second is a deep 1-D convolution neural network that includes a softmax layer. The experimental results obtained using real-time motor stator current data demonstrate the effectiveness of the proposed methods for real-time monitoring of motor conditions. The results also demonstrate that the proposed methods can effectively diagnose five different motor states, including two different demagnetization fault states and two bearing fault states.
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