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.