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

Zhang, Xiaotian, Hu, Yihua, Deng, Jiamei et al. (2 more authors) (2022) Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis:A Review. IEEE Access. pp. 29069-29088. ISSN 2169-3536

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Zhang, Xiaotian
  • Hu, Yihua (yh2137@york.ac.uk)
  • Deng, Jiamei
  • Xu, Hui (hx853@york.ac.uk)
  • Wen, Huiqing
Copyright, Publisher and Additional Information:

Publisher Copyright: © 2013 IEEE.

Keywords: Artificial intelligence,fault diagnosis,feature extraction,machine learning algorithms,neural networks
Dates:
  • Published: 8 March 2022
  • Accepted: 4 March 2022
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Electronic Engineering (York)
Depositing User: Pure (York)
Date Deposited: 18 Jul 2022 11:40
Last Modified: 21 Jan 2025 18:03
Published Version: https://doi.org/10.1109/ACCESS.2022.3157820
Status: Published
Refereed: Yes
Identification Number: 10.1109/ACCESS.2022.3157820
Related URLs:
Open Archives Initiative ID (OAI ID):

Download

Filename: Feature_Engineering_and_Artificial_Intelligence_Supported_Approaches_Used_for_Electric_Powertrain_Fault_Diagnosis_A_Review.pdf

Description: Feature_Engineering_and_Artificial_Intelligence-Supported_Approaches_Used_for_Electric_Powertrain_Fault_Diagnosis_A_Review

Licence: CC-BY 2.5

Export

Statistics