Fault diagnosis of permanent magnet synchronous motor

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.