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64 / 2021-07-18 21:16:07
Interturn Short Circuit Fault Diagnosis of Brushless DC Motor Based on Image Feature Extraction and Transfer Learning
Data Processing Techniques
终稿
Jiliang Wang / Anhui University
Hui Wang / Anhui University
Siliang Lu / Anhui University
Haidong Shao / Hunan University
         In this paper, a method for interturn short circuit fault diagnosis of brushless DC motor (BLDCM) based on image feature extraction and transfer learning is proposed. First of all, the three-phase current signals of the motor stator windings are collected synchronously to convert the one-dimensional current signals into two-dimensional image signals. Then the convolutional neural network based on transfer learning is used to diagnose the interturn short circuit fault. This method reduces the cost of stator winding fault detection in permanent magnet motor system and has potential application value in precise location and diagnosis of stator winding fault.
重要日期

  • 会议日期

    10月21日

    2021

    至

    10月23日

    2021

  • 10月26日 2021

    注册截止日期

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Southeast University, China
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