Demagnetization Fault Binary Classification of Permanent Magnet Motors Using ML Classifiers.

In electric vehicle applications, early detection of demagnetization faults is crucial for ensuring the smooth operation of Permanent Magnet Synchronous Motors (PMSM). Indeed, an efficient maintenance operation can be carried out with the assistance of defect identification and classification. In this paper, Machine Learning Classifiers (MLC) based demagnetization fault binary classification for PMSM using motor current signal spectral analysis are presented. Threephase current signals are obtained by building a Finite Elements (FE) model with predefined demagnetization faults in order to obtain currents data. The Power Spectral Density (PSD) is used to extract the Amplitude of Sideband Components (ASBCs) from the frequency pattern. In order to classify the demagnetization fault state, different MLC are finally trained and evaluated by using the extracted feature set. The MLC exhibits really encouraging outcomes for accuracy and other recognized performance metrics. The suggested approach outperforms prior research studies with an accuracy that is marginally higher than 99%. [ABSTRACT FROM AUTHOR]