TSoft-Net: A novel transfer soft thresholding network based on self-attention for intelligent fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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TSoft-Net: A novel transfer soft thresholding network based on self-attention for intelligent fault diagnosis of rotating machinery

Published:2024-03 Issue: Volume:227 Page:114237
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Yu Shihang^ORCID,Pang Shanchen,Song Limei,Wang Min,He Sicheng,Wu Wenhao

Publisher

Elsevier BV

Reference46 articles.

1. Synchronous odd symmetric transform for rolling bearing fault diagnosis;Cui;Measurement,2024

2. A quantitative detection method for wheel polygonization of heavy-haul locomotives based on a hybrid deep learning model;Dong;Measurement,2024

3. Interactive dual adversarial neural network framework: An open-set domain adaptation intelligent fault diagnosis method of rotating machinery;Mao;Measurement,2022

4. A novel strategy using optimized MOMED and B-spline based envelope-derivative operator for compound fault detection of the rolling bearing;Xu;Struct. Health Monit.,2022

5. An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis;Chauhan;Measurement,2024

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Insights into modern machine learning approaches for bearing fault classification: A systematic literature review;Results in Engineering;2024-09

2. Editorial: Application of digital twin technology in prognostic and health management of complex machineries;Measurement;2024-08

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