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DRSwin-ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with Shifted Windows

  • Published:2024-10 Issue: Volume:250 Page:110327
  • ISSN:0951-8320
  • Container-title:Reliability Engineering & System Safety
  • language:en
  • Short-container-title:Reliability Engineering & System Safety

Author:

Zhou Tao,Yao Dechen,Yang Jianwei,Meng Chang,Li Ankang,Li Xi

Funder

Beijing University of Civil Engineering and Architecture

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Publisher

Elsevier BV

Reference33 articles.

1. A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis;Zheng;Reliab Eng Syst Saf,2024

2. Rotating machinery prognostics: state of the art, challenges and opportunities;Heng;Mech Syst Signal Process,2009

3. Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing;Wei;IEEE Trans Intell Trans Syst,2023

4. Flexible generalized demodulation for intelligent bearing fault diagnosis under nonstationary conditions;Liu;IEEE Trans Industr Inform,2023

5. Maintenance optimisation of multi-unit balanced systems using deep reinforcement learning;Liu;Reliab Eng Syst Saf,2023

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