On the Accuracy of Rotor Demagnetization Fault Detection in PMSM Using Vibration-Based Condition Indicators

Abstract

Permanent magnet (PM) synchronous motor (PMSM) is widely employed in trams, robot industries, and national defense applications. The demagnetization of the rotor magnet would cause adverse effects on the operation of PMSM. By implementing fault monitoring and severity evaluation of the rotor magnet, unscheduled maintenance and economic losses caused by demagnetization fault can be avoided. This paper introduces a rotor demagnetization fault diagnosis and severity evaluation method based on vibrating signal analysis. The vibration analysis model of rotor demagnetization fault is developed. Firstly, the fast Fourier transform is applied to analyze vibration signals. Meanwhile, frequency-domain fault features are extracted as condition indicators (CIs) for PMSM demagnetization monitoring. A statistical diagnosis method based on Chebyshev’s inequality is then presented to identify the PMSM demagnetization fault. Finally, the machine learning classification algorithm based on linear discriminant analysis (LDA) is used to identify the demagnetization degree. Experimental results show that the proposed fault diagnosis approach can provide an excellent solution for the severity evaluation of PMSM demagnetization fault.