Online Diagnosis and Severity Estimation of Partial and Uniform Irreversible Demagnetization Fault in Interior Permanent Magnet Synchronous Motor

This paper presents the online detection, separation and severity estimation of the uniform and partial irreversible demagnetization fault (IDF) in interior permanent magnet synchronous motor (IPMSM) using the frequency spectrum of the input-voltage. Due to IDF the fundamental component of the voltage reduce and the partial IDF cause additional even order harmonics in the input-voltage. In this study, the fundamental component and the even order harmonics are used as the fault indicators. Furthermore, the increase in the ratio of fundamental to fifth order harmonic due to IDF is used to train an artificial neural network based algorithm and estimate the severity of a relatively small degree of IDF in the IPMSM. The proposed method has the ability to cover most of the operating points of the benchmark IPMSM and also able to distinguish between IDF and eccentricity fault. Finite element method based simulation and experiment on a 400-watt IPMSM is performed to verify the proposed method.