A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM
Due to operating conditions and natural aging, the demagnetization of permanent magnets (PM) can significantly impact the performance of permanent magnet synchronous motors (PMSM), potentially leading to reduced output and motor damage. In response to this challenge, a comprehensive PM health manage...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824014881 |
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| author | Huizhen Wang Lei Wang QiYa Wu Haoying Pei Lijun Diao |
| author_facet | Huizhen Wang Lei Wang QiYa Wu Haoying Pei Lijun Diao |
| author_sort | Huizhen Wang |
| collection | DOAJ |
| description | Due to operating conditions and natural aging, the demagnetization of permanent magnets (PM) can significantly impact the performance of permanent magnet synchronous motors (PMSM), potentially leading to reduced output and motor damage. In response to this challenge, a comprehensive PM health management framework is proposed, applying data-driven algorithms to monitor the state of PMs based on raw stator current data. First, the proposed approach anomaly detection requires only normal data during the training phase, addressing the practical issue of limited fault samples. Furthermore, ensemble learning integrates the outputs of multiple base learners to enhance generalization. Second, fault diagnosis is carried out using the Genetic Algorithm-Support Vector Machine (GA-SVM) to accurately classify demagnetization faults. Finally, the Pauta criterion is applied to process the intermediate Support Vector Domain Description (SVDD) variables, generating anomaly scores that classify PMSM health states based on segmented thresholds. Compared to existing methods, this system provides a more comprehensive and autonomous PMSM state recognition, reducing the reliance on expert judgment and minimizing the risk of failure. Through FEM+MATLAB simulations under various demagnetization and load conditions demonstrate the framework's effectiveness in monitoring PM health. This approach offers a cost-effective and intelligent maintenance solution, enhancing the reliability and longevity of PMSM systems. |
| format | Article |
| id | doaj-art-17f3cb9f2aad4c84b9c42df86ef1fc55 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-17f3cb9f2aad4c84b9c42df86ef1fc552025-02-07T04:47:04ZengElsevierAlexandria Engineering Journal1110-01682025-02-01113331346A data-driven intelligent fault diagnosis framework for permanent magnet in PMSMHuizhen Wang0Lei Wang1QiYa Wu2Haoying Pei3Lijun Diao4School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Lab. of Vehicular Multi-Energy Drive Systems (Beijing Jiaotong University), Ministry of Education, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Lab. of Vehicular Multi-Energy Drive Systems (Beijing Jiaotong University), Ministry of Education, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Lab. of Vehicular Multi-Energy Drive Systems (Beijing Jiaotong University), Ministry of Education, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Lab. of Vehicular Multi-Energy Drive Systems (Beijing Jiaotong University), Ministry of Education, Beijing 100044, ChinaSchool of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Key Lab. of Vehicular Multi-Energy Drive Systems (Beijing Jiaotong University), Ministry of Education, Beijing 100044, China; Corresponding author at: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China.Due to operating conditions and natural aging, the demagnetization of permanent magnets (PM) can significantly impact the performance of permanent magnet synchronous motors (PMSM), potentially leading to reduced output and motor damage. In response to this challenge, a comprehensive PM health management framework is proposed, applying data-driven algorithms to monitor the state of PMs based on raw stator current data. First, the proposed approach anomaly detection requires only normal data during the training phase, addressing the practical issue of limited fault samples. Furthermore, ensemble learning integrates the outputs of multiple base learners to enhance generalization. Second, fault diagnosis is carried out using the Genetic Algorithm-Support Vector Machine (GA-SVM) to accurately classify demagnetization faults. Finally, the Pauta criterion is applied to process the intermediate Support Vector Domain Description (SVDD) variables, generating anomaly scores that classify PMSM health states based on segmented thresholds. Compared to existing methods, this system provides a more comprehensive and autonomous PMSM state recognition, reducing the reliance on expert judgment and minimizing the risk of failure. Through FEM+MATLAB simulations under various demagnetization and load conditions demonstrate the framework's effectiveness in monitoring PM health. This approach offers a cost-effective and intelligent maintenance solution, enhancing the reliability and longevity of PMSM systems.http://www.sciencedirect.com/science/article/pii/S1110016824014881PMSMDemagnetizationFault diagnosisStator phase currentCondition monitoringHealth evaluation |
| spellingShingle | Huizhen Wang Lei Wang QiYa Wu Haoying Pei Lijun Diao A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM Alexandria Engineering Journal PMSM Demagnetization Fault diagnosis Stator phase current Condition monitoring Health evaluation |
| title | A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM |
| title_full | A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM |
| title_fullStr | A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM |
| title_full_unstemmed | A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM |
| title_short | A data-driven intelligent fault diagnosis framework for permanent magnet in PMSM |
| title_sort | data driven intelligent fault diagnosis framework for permanent magnet in pmsm |
| topic | PMSM Demagnetization Fault diagnosis Stator phase current Condition monitoring Health evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824014881 |
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