Deep adversarial domain adaptation model for bearing fault diagnosis

Liu, Z.-H., Lu, B.-L., Wei, H.-L. et al. (3 more authors) (2021) Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (7). pp. 4217-4226. ISSN 2168-2216



Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: fault diagnosis; bearing; feature extraction; stack auto-encoder (SAE); unsupervised learning; domain adaptation; adversarial network; machine learning; deep learning; deep neural networks
  • Accepted: 18 July 2019
  • Published (online): 19 August 2019
  • Published: July 2021
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: 12 Nov 2021 14:28
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: