Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach

Liu, Z.-H., Lu, B.-L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (2 more authors) (2019) Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach. IEEE Sensors Journal, 19 (24). pp. 12261-12270. ISSN 1530-437X

Abstract

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

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Keywords: fault diagnosis; electromechanical drivetrain; deep neural network; deep learning; domain adaptation (DA); joint distribution optimal; auto-encoder(AE); machine learning; artificial intelligence; bearing; gearboxes; wind turbine; varying working conditions
Dates:
  • Published: 15 December 2019
  • Published (online): 4 September 2019
  • Accepted: 30 August 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
Funder
Grant number
Engineering and Physical Science Research Council (EPSRC)
EP/I011056/1; EP/H00453X/1
National Natural Science Foundation of China
61972443; 61573299; 61503134
Hunan Provincial Hu-Xiang Young Talents Project of China
2018RS3095
Hunan Provincial Natural Science Foundation of China
2018JJ2134
Depositing User: Symplectic Sheffield
Date Deposited: 30 Sep 2019 10:55
Last Modified: 12 Nov 2021 14:34
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.1109/jsen.2019.2939360
Open Archives Initiative ID (OAI ID):

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