A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems
The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modifie...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/2087027 |
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| _version_ | 1850165984585842688 |
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| author | Zhu Huang Tao Wang Wei Liu Luis Valencia-Cabrera Mario J. Pérez-Jiménez Pengpeng Li |
| author_facet | Zhu Huang Tao Wang Wei Liu Luis Valencia-Cabrera Mario J. Pérez-Jiménez Pengpeng Li |
| author_sort | Zhu Huang |
| collection | DOAJ |
| description | The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-e4616b571e3e4ca5a4e15a0a824aa5c4 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e4616b571e3e4ca5a4e15a0a824aa5c42025-08-20T02:21:34ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/20870272087027A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P SystemsZhu Huang0Tao Wang1Wei Liu2Luis Valencia-Cabrera3Mario J. Pérez-Jiménez4Pengpeng Li5School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, ChinaSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, ChinaSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, ChinaResearch Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Sevilla 41012, SpainResearch Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Sevilla 41012, SpainTaizhou Power Supply Company, State Grid Zhejiang Electric Power Co. Ltd., Taizhou 318000, ChinaThe fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.http://dx.doi.org/10.1155/2021/2087027 |
| spellingShingle | Zhu Huang Tao Wang Wei Liu Luis Valencia-Cabrera Mario J. Pérez-Jiménez Pengpeng Li A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems Complexity |
| title | A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems |
| title_full | A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems |
| title_fullStr | A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems |
| title_full_unstemmed | A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems |
| title_short | A Fault Analysis Method for Three-Phase Induction Motors Based on Spiking Neural P Systems |
| title_sort | fault analysis method for three phase induction motors based on spiking neural p systems |
| url | http://dx.doi.org/10.1155/2021/2087027 |
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