Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions
Detection and isolation of multiple low-severity faults in permanent magnet synchronous machines (PMSMs) under dynamics and noisy conditions are very important to enhance the system’s reliability, lifetime, and service availability. This study investigates a robust fault classification sc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11068985/ |
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| Summary: | Detection and isolation of multiple low-severity faults in permanent magnet synchronous machines (PMSMs) under dynamics and noisy conditions are very important to enhance the system’s reliability, lifetime, and service availability. This study investigates a robust fault classification scheme for PMSMs under low-severity, multiple-fault conditions in noisy and variable-speed scenarios. Two supervised learning models, namely Extra Trees (ET) and Support Vector Machine (SVM), are developed and compared using time-domain (TD) and frequency-domain (FD) features. A hybrid feature extraction method is adopted to balance the computational burden and classification accuracy. Hyperparameters and frequency-domain (FD) features are optimized based on prediction confidence, prediction times, and accuracy. Furthermore, the robustness of the data-driven models is evaluated at different Signal-to-Noise (SNR) ratios under mixed faults and variable speeds in an in-house PMSM, combining an inter-turn short circuit fault of 2.2% in one phase and a 30% demagnetization of one rotor magnet. A comparative study was conducted, confirming the proposed ET model’s superior performance in terms of prediction accuracy and robustness. |
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| ISSN: | 2169-3536 |