Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation-Reference-Cited by-同舟云学术

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Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation

Published:2025-02 Issue: Volume:241 Page:115714
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Wang Youming^ORCID,Huang Yirun

Publisher

Elsevier BV

Reference49 articles.

1. Multi-scale deep neural network approach with attention mechanism for remaining useful life estimation;Kara;Comput. Ind. Eng.,2022

2. A review of vibration-based gear wear monitoring and prediction techniques;Feng;Mech. Syst. Sig. Process.,2023

3. A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model;Xu;Comput. Ind. Eng.,2023

4. A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems;Feng;Int. J. Fatigue,2023

5. Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery;Wang;Neurocomputing,2020

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