Qiu, Wangde, Zhao, Xing orcid.org/0000-0003-4000-0446, Tyrrell, Andy orcid.org/0000-0002-8533-2404 et al. (3 more authors) (2024) Application of Artificial Intelligence-Based Technique in Electric Motors:A Review. IEEE Transactions on Power Electronics. pp. 13543-13568. ISSN 0885-8993
Abstract
Electric motors find widespread application across various industrial fields. The pursuit of enhanced comprehensive electric motors performance has consistently drawn significant attention, prompting extensive research in this domain. With the rapid and profound integration of artificial intelligence (AI) technology into diverse sectors, an increasing array of AI-based methodologies is being employed in electric motors research. This review aims to comprehensively delineate the applications of AI within the realm of electric motors, specifically focusing into three pivotal stages: electric motors design, drive control, and health maintenance, covering a full-cycle research and development of electric motors. The review commences by outlining the elucidation of the fundamental principles and components of AI. Subsequently, the encompassed input signals, characteristic methodologies, and AI techniques are reviewed and analyzed as demonstrated by pertinent research cases. Furthermore, a synthesis of the distinctive attributes characterizing various techniques is presented. Conclusively, this article offers an unprecedented review of AI-based technology throughout the entirety of the electric motors research cycle. Considering the existing body of knowledge, this article stands as the pioneering endeavor to encapsulate the expansive domain of AI's influence in electric motors research.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | Artificial intelligence (AI),design,drive control,electric motors,maintenance |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 05 Jun 2024 10:50 |
Last Modified: | 03 Jun 2025 23:12 |
Published Version: | https://doi.org/10.1109/TPEL.2024.3410958 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/TPEL.2024.3410958 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213146 |
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