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Wind turbine pitch bearing fault detection with Bayesian augmented temporal convolutional networks

Author(s): (Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK)
ORCID (Department of Electrical and Electronic Engineering, University of Manchester, Manchester, UK)
Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 2, v. 23
Page(s): 1089-1106
DOI: 10.1177/14759217231175886
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/14759217231175886.
  • About this
    data sheet
  • Reference-ID
    10739188
  • Published on:
    03/09/2023
  • Last updated on:
    25/04/2024
 
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