Deep adversarial domain adaptation model for bearing fault diagnosis

Liu, Z.-H., Lu, B.-L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2019) Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems. pp. 1-10. ISSN 2168-2216

Abstract

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Authors/Creators:
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Keywords: fault diagnosis; bearing; feature extraction; stack auto-encoder (SAE); unsupervised learning; domain adaptation; adversarial network; machine learning; deep learning; deep neural networks
Dates:
  • Accepted: 18 July 2019
  • Published (online): 19 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:
FunderGrant number
National Natural Science Foundation of China61503134; 61573299
Hunan Provincial Young Talents Project2018RS3095
Hunan Provincial Natural Science Foundation of China13JJ8014
Depositing User: Symplectic Sheffield
Date Deposited: 30 Sep 2019 10:44
Last Modified: 19 Aug 2020 00:38
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: https://doi.org/10.1109/tsmc.2019.2932000

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