Online Diagnosis of Typical Winding Faults in Permanent Magnet Synchronous Motors under Complex Working Conditions

Winding faults seriously affect the normal operation of permanent magnet synchronous motors. As one of the most widely used fault diagnosis methods, motor current signature analysis (MCSA) can effectively diagnose winding faults. However, its accuracy can be affected by complex operating conditions, sensor errors, and calculation errors. An online diagnosis method based on controller signals is proposed to ensure accurate fault diagnosis under complex working conditions without additional sensors. Firstly, winding faults are analyzed mechanically, and the transmission of fault information among the controller signals is revealed. When a winding failure occurs in the motor, the third harmonic appears in the phase current. After the Clarke and Park transform, the third harmonic component appears in the current in the αβ axis, making the second and fourth current harmonics appear in the dq axis. According to the dq-axis voltage-current equation and Park’s inverse transform, the dq-axis reference voltage has anomalous second and fourth harmonics, and the αβ-axis reference voltage has anomalous third harmonic. Secondly, the fault features are extracted through an adaptive random window based on a fast Fourier transform (FFT). An analysis of fault characteristic distribution under single and complex working conditions reveals that some fault characteristics may fail under low-speed working conditions. Based on the artificial neural network (ANN), a deep optimization ANN is proposed. By the batch normalization (BN) algorithm, the deep network structure is residual to improve network generalization ability and diagnostic accuracy. Finally, a performance index of fault characteristics under complex working conditions is defined. The experimental results show that when the motor operates at 500 r/min to 1 250 r/min and 20 N·m to 40 N·m, the fault feature under single operating conditions approximately meets the normal distribution. The fault feature stability (Sf) under complex working conditions is defined based on the distribution variance. By the 3σ principle, this paper determines the distribution interval of fault features and defines the fault feature effectiveness (Ef) under complex working conditions according to class spacing. Moreover, the aliasing penalty factor is introduced to deepen the impact of aliasing. 13 fault features caused by the third harmonic of the three-phase current are taken as the object, and their Sf and Ef are calculated. When Sf is greater than 95% and Ef is greater than 0, the diagnostic accuracy is greater than 95%. Compared with three traditional diagnostic networks by random forest, the diagnostic accuracy of the deep optimization ANN is better, especially for the multi-fault comprehensive diagnosis of winding faults. The following conclusions can be drawn. (1) During motor operation, fault features can fluctuate seriously, especially at low speeds. Some fault features may not be suitable for online fault diagnosis. (2) Before fault diagnosis, calculating Sf and Ef helps filter out less fluctuating fault features, ensuring the accuracy of online diagnosis. (3) If the fault features are subject to large fluctuations from external factors, the deep optimization ANN exhibits higher diagnostic accuracy and generalization ability than the traditional diagnostic networks. © 2024 China Machine Press. All rights reserved.