Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review
Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9730925/ |
| _version_ | 1818442483419316224 |
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| author | Xiaotian Zhang Yihua Hu Jiamei Deng Hui Xu Huiqing Wen |
| author_facet | Xiaotian Zhang Yihua Hu Jiamei Deng Hui Xu Huiqing Wen |
| author_sort | Xiaotian Zhang |
| collection | DOAJ |
| description | Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed. |
| first_indexed | 2024-12-14T18:44:52Z |
| format | Article |
| id | doaj.art-92b69eda4a964115b1221306e2315bda |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T18:44:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-92b69eda4a964115b1221306e2315bda2022-12-21T22:51:24ZengIEEEIEEE Access2169-35362022-01-0110290692908810.1109/ACCESS.2022.31578209730925Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A ReviewXiaotian Zhang0https://orcid.org/0000-0002-0126-6834Yihua Hu1https://orcid.org/0000-0002-1007-1617Jiamei Deng2https://orcid.org/0000-0002-0989-1685Hui Xu3Huiqing Wen4https://orcid.org/0000-0002-0169-488XDepartment of Electronic Engineering, University of York, York, U.K.Department of Electronic Engineering, University of York, York, U.K.School of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds, U.K.School of Built Environment, Engineering, and Computing, Leeds Beckett University, Leeds, U.K.Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaElectric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.https://ieeexplore.ieee.org/document/9730925/Artificial intelligencefeature extractionfault diagnosisneural networksmachine learning algorithms |
| spellingShingle | Xiaotian Zhang Yihua Hu Jiamei Deng Hui Xu Huiqing Wen Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review IEEE Access Artificial intelligence feature extraction fault diagnosis neural networks machine learning algorithms |
| title | Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review |
| title_full | Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review |
| title_fullStr | Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review |
| title_full_unstemmed | Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review |
| title_short | Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review |
| title_sort | feature engineering and artificial intelligence supported approaches used for electric powertrain fault diagnosis a review |
| topic | Artificial intelligence feature extraction fault diagnosis neural networks machine learning algorithms |
| url | https://ieeexplore.ieee.org/document/9730925/ |
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