Transfer reinforcement learning method with multi-label learning for compound fault recognition-Reference-Cited by-同舟云学术

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Transfer reinforcement learning method with multi-label learning for compound fault recognition

Published:2023-01 Issue: Volume:55 Page:101818
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Wang Zisheng,Zhang Qing,Tang Lv,Shi Tielin,Xuan Jianping

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems,Building and Construction

Reference47 articles.

1. Hierarchical diagnosis of bearing faults using branch convolutional neural network considering noise interference and variable working conditions;Su;Knowl.-Based Syst.,2021

2. A benchmark of measurement approaches to track the natural evolution of spall severity in rolling element bearings;Zhang;Mech. Syst. Signal Process.,2022

3. The Harmogram: A periodic impulses detection method and its application in bearing fault diagnosis;Zhang;Mech. Syst. Signal Process.,2022

4. Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation;Yao;Mech. Syst. Signal Process.,2022

5. Electromechanical modeling of a motor–gearbox system for local gear tooth faults detection;El Yousfi;Mech. Syst. Signal Process.,2022

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