A regularized LSTM method for predicting remaining useful life of rolling bearings

Liu, Z.-H., Meng, X.-D., Wei, H.-L. orcid.org/0000-0002-4704-7346 et al. (4 more authors) (2021) A regularized LSTM method for predicting remaining useful life of rolling bearings. International Journal of Automation and Computing, 18 (4). pp. 581-593. ISSN 1476-8186



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Keywords: Deep learning; fault diagnosis; fault prognosis; long and short time memory network (LSTM); rolling bearing; rotating machinery; regularization; remaining useful life prediction (RUL); recurrent neural network (RNN)
  • Accepted: 30 December 2020
  • Published (online): 8 March 2021
  • Published: August 2021
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: 14 Jul 2022 11:01
Last Modified: 14 Jul 2022 11:01
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: https://doi.org/10.1007/s11633-020-1276-6

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