Bi-Generator Cooperative Domain Adversarial Neural Network for Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

Bi-Generator Cooperative Domain Adversarial Neural Network for Bearing Fault Diagnosis

Published:2024-04-01 Issue:7 Volume:24 Page:10584-10593
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Jingde¹,Shen Changqing¹^ORCID,Shi Juanjuan¹^ORCID,Li Chuan²^ORCID,Wang Dong³^ORCID,Zhu Zhongkui¹^ORCID

Affiliation:

1. School of Rail Transportation, Soochow University, Suzhou, China

2. School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, China

3. Department of Industrial Engineering and Management and the State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Suzhou Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10487994/10424420.pdf?arnumber=10424420

Reference28 articles.

1. Dual-Attention Generative Adversarial Networks for Fault Diagnosis Under the Class-Imbalanced Conditions

2. Adaptive Sparse Representation-Based Minimum Entropy Deconvolution for Bearing Fault Detection

3. A New Adversarial Domain Generalization Network Based on Class Boundary Feature Detection for Bearing Fault Diagnosis

4. Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review

5. A Brief Review of Domain Adaptation

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