volume | PIER Journals

Abstract

Aiming at the problems of weak distinctiveness and low diagnostic accuracy of permanent magnet synchronous motor (PMSM) demagnetization faults, a local demagnetization fault diagnosis method for PMSM based on Improved Empirical Wavelet Transform (IEWT) combined with Sparrow Search Algorithm (SSA) optimized Extreme Learning Machine (ELM) is proposed. Taking the radial leakage magnetic signal on the motor surface as the research object, the leakage magnetic experimental data under 15 different demagnetization states are extracted. To solve the problem of unreasonable spectrum segmentation in the EWT method, an adaptive decomposition with improved frequency band division is performed according to the special spectrum trend of PMSM leakage magnetic signals. Then, the normalized energy values of each intrinsic mode function (IMF) are calculated to form the corresponding feature vectors, which are input into the ELM model optimized by the SSA algorithm for demagnetization state identification. Experimental results show that the method based on IEWT-SSA-ELM has a significant improvement in fault identification effect compared with the unimproved and unoptimized methods.

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Dr. Dehai Chen

Mr. Jinpeng Xu

Dr. Zhijun Li

Dr. Hao Gong