Fault diagnosis of permanent magnet synchronous motor based on IEWT and CatBoost

DOI Web Site 参考文献31件
  • Zhou Lidan
    School of Electrical Engineering, Shanghai University of Electric Power
  • Dai Shufang
    School of Electrical Engineering, Shanghai University of Electric Power
  • Yao Gang
    School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University

抄録

<p>With the development of wind power generation, improving the accuracy of fault diagnosis in permanent magnet synchronous motor (PMSM) is crucial for reducing maintenance costs. This paper integrates signal analysis and machine learning algorithms, proposing an approach that combines the improved empirical wavelet transformation (IEWT) with the CatBoost algorithm to diagnose open-phase faults in PMSM. IEWT effectively suppresses the modal aliasing phenomenon caused by noise and spectral leakage in traditional empirical wavelet transformation, and the decomposition results can demonstrate the time-frequency characteristics of the fault signal used for training the CatBoost classification model. Experimental results indicate that the proposed algorithm achieves high overall accuracy in diagnosing open-phase faults in PMSM, with balanced performance across each category.</p>

収録刊行物

  • IEICE Electronics Express

    IEICE Electronics Express 21 (8), 20240123-20240123, 2024-04-25

    一般社団法人 電子情報通信学会

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