Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines
Artificial intelligence (AI) shows good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. This paper proposes an effective approach...
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| Format: | Article |
| Language: | English |
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Amirkabir University of Technology
2024-02-01
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| Series: | AUT Journal of Electrical Engineering |
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| Online Access: | https://eej.aut.ac.ir/article_5180_5add79b1ec22d6b97a1b59a6a7c8e68c.pdf |
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| author | Seyed Hamid Rafiei Mansoor Ojaghi Mahdi Sabouri |
| author_facet | Seyed Hamid Rafiei Mansoor Ojaghi Mahdi Sabouri |
| author_sort | Seyed Hamid Rafiei |
| collection | DOAJ |
| description | Artificial intelligence (AI) shows good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. This paper proposes an effective approach to solve this problem by getting the required training data from exact simulation results. To evaluate this idea, the finite elements method is used to simulate a three-phase induction motor (IM) in the healthy state as well as the stator inter-turn fault, broken rotor bar fault, and mixed eccentricity fault conditions. Then, for every fault condition, some fault indices are extracted from the stator line current and used to arrange and train a suitable support vector machine (SVM) model to detect and discriminate the fault condition. A similar IM is prepared in the laboratory, where, its stator line currents are sampled and recorded under the healthy and the fault conditions, and the same fault indices are extracted from the stator currents. Some penalties, which are determined by comparing experimental test results and corresponding simulation results in the healthy state, are applied to the experimentally attained values of the indices. The modified indices are then applied to the trained SVM models, where, the attained results confirm the trained SVM models are equally able to detect and discriminate the faults in the real IMs. |
| format | Article |
| id | doaj-art-91df217fa1f14f81ba06f286f39b62be |
| institution | Kabale University |
| issn | 2588-2910 2588-2929 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Amirkabir University of Technology |
| record_format | Article |
| series | AUT Journal of Electrical Engineering |
| spelling | doaj-art-91df217fa1f14f81ba06f286f39b62be2025-08-20T03:27:43ZengAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-29102588-29292024-02-0156Issue 1 (Special Issue)577810.22060/eej.2023.22349.55345180Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical MachinesSeyed Hamid Rafiei0Mansoor Ojaghi1Mahdi Sabouri2Department of Electrical Engineering, University of Zanjan, Zanjan, IranDepartment of Electrical Engineering, University of Zanjan, Zanjan, IranDepartment of Electrical and Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.Artificial intelligence (AI) shows good potential for detecting and discriminating faults in electrical machines, however, they require initial training with sufficient data, which is almost impossible to collect for working electrical machines in the field. This paper proposes an effective approach to solve this problem by getting the required training data from exact simulation results. To evaluate this idea, the finite elements method is used to simulate a three-phase induction motor (IM) in the healthy state as well as the stator inter-turn fault, broken rotor bar fault, and mixed eccentricity fault conditions. Then, for every fault condition, some fault indices are extracted from the stator line current and used to arrange and train a suitable support vector machine (SVM) model to detect and discriminate the fault condition. A similar IM is prepared in the laboratory, where, its stator line currents are sampled and recorded under the healthy and the fault conditions, and the same fault indices are extracted from the stator currents. Some penalties, which are determined by comparing experimental test results and corresponding simulation results in the healthy state, are applied to the experimentally attained values of the indices. The modified indices are then applied to the trained SVM models, where, the attained results confirm the trained SVM models are equally able to detect and discriminate the faults in the real IMs.https://eej.aut.ac.ir/article_5180_5add79b1ec22d6b97a1b59a6a7c8e68c.pdfartificial intelligenceelectrical machinesfault diagnosisfinite elements methodtraining data |
| spellingShingle | Seyed Hamid Rafiei Mansoor Ojaghi Mahdi Sabouri Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines AUT Journal of Electrical Engineering artificial intelligence electrical machines fault diagnosis finite elements method training data |
| title | Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines |
| title_full | Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines |
| title_fullStr | Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines |
| title_full_unstemmed | Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines |
| title_short | Effective Approach to Use Artificial Intelligence for Detecting Different Faults in Working Electrical Machines |
| title_sort | effective approach to use artificial intelligence for detecting different faults in working electrical machines |
| topic | artificial intelligence electrical machines fault diagnosis finite elements method training data |
| url | https://eej.aut.ac.ir/article_5180_5add79b1ec22d6b97a1b59a6a7c8e68c.pdf |
| work_keys_str_mv | AT seyedhamidrafiei effectiveapproachtouseartificialintelligencefordetectingdifferentfaultsinworkingelectricalmachines AT mansoorojaghi effectiveapproachtouseartificialintelligencefordetectingdifferentfaultsinworkingelectricalmachines AT mahdisabouri effectiveapproachtouseartificialintelligencefordetectingdifferentfaultsinworkingelectricalmachines |