Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis-Reference-Cited by-同舟云学术

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Multiscale Channel Attention-Driven Graph Dynamic Fusion Learning Method for Robust Fault Diagnosis

Published:2024-09 Issue:9 Volume:20 Page:11002-11013
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhang Xin¹^ORCID,Liu Jie²^ORCID,Zhang Xi³^ORCID,Lu Yanglong¹^ORCID

Affiliation:

1. School of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China

2. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, China

3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Funder

University Grants Committee of Hong Kong

HKUST-Bright Dream Robotics Joint Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10535760.pdf?arnumber=10535760

Reference32 articles.

1. From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where

2. Review on Diagnosis Techniques for Intermittent Faults in Dynamic Systems

3. Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis

4. A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis

5. Fault Diagnosis of Rolling Bearings Based on an Improved Stack Autoencoder and Support Vector Machine

Cited by 3 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. Multi-source domain self-supervised enhanced transfer fault diagnosis approach with source sample refinement strategy;Reliability Engineering & System Safety;2024-11

2. Spatial-temporal graph feature learning driven by time–frequency similarity assessment for robust fault diagnosis of rotating machinery;Advanced Engineering Informatics;2024-10

3. Fault diagnosis of rolling bearing based on adaptive attention network and federated learning;Measurement Science and Technology;2024-08-14

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