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. ISSN 1530-437X

Abstract

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Authors/Creators:
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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:
  • Accepted: 30 August 2019
  • Published (online): 4 September 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:
FunderGrant number
Engineering and Physical Science Research Council (EPSRC)EP/I011056/1; EP/H00453X/1
National Natural Science Foundation of China61972443; 61573299; 61503134
Hunan Provincial Hu-Xiang Young Talents Project of China2018RS3095
Hunan Provincial Natural Science Foundation of China2018JJ2134
Depositing User: Symplectic Sheffield
Date Deposited: 30 Sep 2019 10:55
Last Modified: 04 Sep 2020 00:39
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/jsen.2019.2939360

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