Detection of Demagnetization Faults in Electric Motors by Analyzing Inverter Based Current Data Using Machine Learning Techniques

Abstract

Demagnetization of the rotor magnets is a significant failure mode that can occur in permanent magnet synchronous machines (PMSMs). Early detection of demagnetization faults can help change system parameters to reduce power output or ensure safety. In this paper, the effects of demagnetization faults were analyzed both in simulation and experiments using the example of drone motors. An approach was investigated to detect even minor demagnetization faults that does not require any additional sensing effort. Machine learning (ML) techniques are used to analyze the phase current data directly received from the inverter to enable anomaly detection. For this purpose, the phase current is transformed by the Fast Fourier Transform (FFT), the spectral data is then reduced in dimensionality, followed by an anomaly detection algorithm using a one-class support vector machine (OC-SVM). To ensure simplified initialization of the ML model without the need for training sets of damaged drives, only data from magnetically undamaged motors was used to train the models for anomaly detection. Different selections of considered harmonics and different metrics were investigated using the experimental data, achieving a precision of up to 99%, a specificity of up to 98%, and an accuracy of up to 90%.

Keywords

• anomaly detection
• cognitive power electronics
• demagnetization
• electric motor
• fault detection
• Kernel Principal Component Analysis