Fault Diagnosis Method of Permanent Magnet Synchronous Motor Demagnetization and Eccentricity Based on Branch Current
Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/16/4/223 |
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| Summary: | Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. |
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| ISSN: | 2032-6653 |