Supervised Contrastive Learning-Based Domain Adaptation Network for Intelligent Unsupervised Fault Diagnosis of Rolling Bearing-Reference-Cited by-同舟云学术

Supervised Contrastive Learning-Based Domain Adaptation Network for Intelligent Unsupervised Fault Diagnosis of Rolling Bearing

Published:2022-12 Issue:6 Volume:27 Page:5371-5380
ISSN:1083-4435
Container-title:IEEE/ASME Transactions on Mechatronics
language:
Short-container-title:IEEE/ASME Trans. Mechatron.

Author:

Zhang Yongchao¹^ORCID,Ren Zhaohui¹^ORCID,Zhou Shihua¹,Feng Ke²^ORCID,Yu Kun³^ORCID,Liu Zheng²^ORCID

Affiliation:

1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

2. School of Engineering, The University of British Columbia, Kelowna, BC, Canada

3. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

Funder

China Scholarship Council

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/3516/9985402/09802514.pdf?arnumber=9802514

Reference37 articles.

1. Residual joint adaptation adversarial network for intelligent transfer fault diagnosis

2. Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

3. A Hybrid Generalization Network for Intelligent Fault Diagnosis of Rotating Machinery Under Unseen Working Conditions

4. Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task;huang;IEEE Trans Instrum Meas,2020

5. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

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