Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

• Published in: IEEE access Vol. 10; pp. 29069 - 29088
• Main Authors: Zhang, Xiaotian, Hu, Yihua, Deng, Jiamei, Xu, Hui, Wen, Huiqing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Condition monitoring; Data mining; Deep learning; Engineering; Fault diagnosis; Feature extraction; Machine learning; machine learning algorithms; Mechanical power transmission; Monitoring; Neural networks; Powertrain; Product safety; Wavelet analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3157820

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.