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

Zhang, Xiaotian, Hu, Yihua ORCID: 0000-0002-1007-1617, Deng, Jiamei, Xu, Hui and Wen, Huiqing (2022) Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review. IEEE ACCESS, 10. pp. 29069-29088.

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Official URL: http://dx.doi.org/10.1109/access.2022.3157820

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Feature extraction, Mechanical power transmission, Artificial intelligence, Fault diagnosis, Monitoring, Data mining, Wavelet analysis, Artificial intelligence, feature extraction, fault diagnosis, neural networks, machine learning algorithms
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	07 Jun 2022 14:15
Last Modified:	15 Mar 2024 12:15
DOI:	10.1109/ACCESS.2022.3157820
Open Access URL:	https://ieeexplore.ieee.org/document/9730925
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156018