A tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery

Liu, Z.-H. orcid.org/0000-0002-6597-4741, Chen, L., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (3 more authors) (2023) A tensor-based domain alignment method for intelligent fault diagnosis of rolling bearing in rotating machinery. Reliability Engineering & System Safety, 230. 108968. ISSN 0951-8320

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 Elsevier. This is an author produced version of a paper subsequently published in Reliability Engineering & System Safety. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Tensor representation; Subspace learning; Tensor alignment; Fault diagnosis; Domain adaptation; Transfer learning; Rolling bearings; Rotating machinery
Dates:
  • Accepted: 7 November 2022
  • Published (online): 9 November 2022
  • Published: February 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 30 Mar 2023 15:19
Last Modified: 09 Nov 2023 01:13
Published Version: http://dx.doi.org/10.1016/j.ress.2022.108968
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.ress.2022.108968

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