A Contrastive Learning-Based Fault Diagnosis Method for Rotating Machinery With Limited and Imbalanced Labels-Reference-Cited by-同舟云学术

A Contrastive Learning-Based Fault Diagnosis Method for Rotating Machinery With Limited and Imbalanced Labels

Published:2023-07-15 Issue:14 Volume:23 Page:16402-16412
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Yan¹^ORCID,Liu Zhuolin¹,Huang Qingqing¹^ORCID

Affiliation:

1. Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, and with the Institute of Industrial Internet, Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Natural Science Foundation of Chongqing

Science and Technology Research Program of Chongqing Municipal Education Commission

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10183770/10151782.pdf?arnumber=10151782

Reference38 articles.

1. A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

2. Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data

3. Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations

4. Contrastive learning based self-supervised time-series analysis

5. A Novel Method for Fault Diagnosis of Bearings with Small and Imbalanced Data Based on Generative Adversarial Networks

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