Fault Detection of Permanent Magnet Synchronous Motor Based on Image Content Retrieval

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Abstract
The motor fault detection based on data-driven approach has achieved some results. However, there are still some problems, such as complex signal processing, long training time and poor adaptability to different working conditions. A new fault detection method for permanent magnet synchronous motor based on image content retrieval is proposed in this paper, which does not require complex signal processing and model training, and has strong adaptability to conditions. First, the fault signals are converted into images by the symmetrized dot pattern method. Second, an image coding technology based on KAZE feature and bag of features is proposed to reduce the computational cost. Then, to improve adaptability of conditions, a cyclic update algorithm of image retrieval library based on image fusion is proposed. Finally, fault detection is realized through image retrieval of feature code matching. Extensive experiments demonstrate the superiority and effectiveness of the proposed method.
Funding Information
  • National Key Research and Development Program of China (2022YFF0608700)
  • National Science Foundation of China (52077064)
  • Postgraduate Scientific Research Innovation Project of Hunan Province (QL20220079)

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