PMSM Demagnetization Fault Diagnosis Method Based on Fuzzy Intelligent Learning of Torque Signals

Abstract

A PMSM demagnetization fault diagnosis method based on fuzzy intelligent learning of torque signals, which includes the following steps of: acquiring torque ripple signals of permanent magnet synchronous motors under different demagnetization faults; calculating a fuzzy membership of the torque ripple signals; decomposing and reconstructing the torque ripple signals by using wavelet packet decomposition to obtain wavelet packet coefficients; calculating the energy of the wavelet packet coefficients, constructing a feature vector sample set with the fuzzy membership, and dividing it into a training set and a test set; constructing Fuzzy Extreme Learning Machine (FELM), and inputting the training set into the FELM for training; inputting the test set into the trained FELM, and calculating classification accuracy. The disclosure solves the problem of unbalanced and irregular training sample distribution by integrating fuzzy theory into the Extreme Learning Machine to fuzzify the torque ripple signal samples under demagnetization fault.

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