Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network
Abstract Permanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective hea...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Electric Power Applications |
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| Online Access: | https://doi.org/10.1049/elp2.12525 |
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| author | Ahmed Mesai Belgacem Mounir Hadef Enas Ali Salah K. Elsayed Prabhu Paramasivam Sherif S. M. Ghoneim |
| author_facet | Ahmed Mesai Belgacem Mounir Hadef Enas Ali Salah K. Elsayed Prabhu Paramasivam Sherif S. M. Ghoneim |
| author_sort | Ahmed Mesai Belgacem |
| collection | DOAJ |
| description | Abstract Permanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective health monitoring techniques for early fault detection are essential to maintain optimal performance and extend the lifespan of these systems. This study presents a qualification‐based methodology for diagnosing faults in three‐phase PMSMs through vibration–current data fusion analysis. The stator faults, specifically inter‐turn short circuits (ITSC) induced via bypassing resistances, were investigated using experimental data from a custom‐built test rig. The collected current and vibration signals were transformed into statistical features. Various operating scenarios were diagnosed utilising a deep regulated neural network (RegNet), an improved convolutional neural network based on an enhanced residual architecture. The proposed approach was assessed through various metrics including training efficiency, precision, recall, f1‐score, and accuracy, and compared against several neural network methods. The findings reveal that the proposed RegNet model achieves perfect accuracy, attaining 100%. This research highlights the efficacy of data fusion analysis and deep learning in fault diagnosis, facilitating proactive maintenance strategies and improving the reliability of PMSMs in diverse industrial applications and renewable energy systems. |
| format | Article |
| id | doaj-art-632d2c729dd749cc87bca3500d5634fd |
| institution | OA Journals |
| issn | 1751-8660 1751-8679 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electric Power Applications |
| spelling | doaj-art-632d2c729dd749cc87bca3500d5634fd2025-08-20T02:00:01ZengWileyIET Electric Power Applications1751-86601751-86792024-12-0118121991200710.1049/elp2.12525Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural networkAhmed Mesai Belgacem0Mounir Hadef1Enas Ali2Salah K. Elsayed3Prabhu Paramasivam4Sherif S. M. Ghoneim5L2EI, Department of Electrical Engineering Faculty of Science and Technology University of Jijel Jijel AlgeriaL2EI, Department of Electrical Engineering Faculty of Science and Technology University of Jijel Jijel AlgeriaChitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh IndiaDepartment of Electrical Engineering College of Engineering Taif University Taif Saudi ArabiaDepartment of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu IndiaDepartment of Electrical Engineering College of Engineering Taif University Taif Saudi ArabiaAbstract Permanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective health monitoring techniques for early fault detection are essential to maintain optimal performance and extend the lifespan of these systems. This study presents a qualification‐based methodology for diagnosing faults in three‐phase PMSMs through vibration–current data fusion analysis. The stator faults, specifically inter‐turn short circuits (ITSC) induced via bypassing resistances, were investigated using experimental data from a custom‐built test rig. The collected current and vibration signals were transformed into statistical features. Various operating scenarios were diagnosed utilising a deep regulated neural network (RegNet), an improved convolutional neural network based on an enhanced residual architecture. The proposed approach was assessed through various metrics including training efficiency, precision, recall, f1‐score, and accuracy, and compared against several neural network methods. The findings reveal that the proposed RegNet model achieves perfect accuracy, attaining 100%. This research highlights the efficacy of data fusion analysis and deep learning in fault diagnosis, facilitating proactive maintenance strategies and improving the reliability of PMSMs in diverse industrial applications and renewable energy systems.https://doi.org/10.1049/elp2.12525fault diagnosispermanent magnet machinesshort‐circuit currents |
| spellingShingle | Ahmed Mesai Belgacem Mounir Hadef Enas Ali Salah K. Elsayed Prabhu Paramasivam Sherif S. M. Ghoneim Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network IET Electric Power Applications fault diagnosis permanent magnet machines short‐circuit currents |
| title | Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network |
| title_full | Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network |
| title_fullStr | Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network |
| title_full_unstemmed | Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network |
| title_short | Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network |
| title_sort | fault diagnosis of inter turn short circuits in pmsm based on deep regulated neural network |
| topic | fault diagnosis permanent magnet machines short‐circuit currents |
| url | https://doi.org/10.1049/elp2.12525 |
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