Self-Supervised Deep Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction of Rolling Bearing Under Unknown Working Condition-Reference-Cited by-同舟云学术

Self-Supervised Deep Domain-Adversarial Regression Adaptation for Online Remaining Useful Life Prediction of Rolling Bearing Under Unknown Working Condition

Published:2023-02 Issue:2 Volume:19 Page:1227-1237
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Mao Wentao¹^ORCID,Chen Jiaxian¹^ORCID,Liu Jing¹^ORCID,Liang Xihui²^ORCID

Affiliation:

1. School of Computer and Information Engineering, Henan Normal University, Xinxiang, China

2. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada

Funder

National Natural Science Foundation of China

Henan Province Technologies Research and Development Project of China

NSFC Development Funding of Henan Normal University

University of Manitoba

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/9424/9989328/09769904.pdf?arnumber=9769904

Reference28 articles.

1. Self-Supervised Feature Learning by Learning to Spot Artifacts

2. Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions

3. Toward Self-Supervised Feature Learning for Online Diagnosis of Multiple Faults in Electric Powertrains

4. Revisiting Self-Supervised Visual Representation Learning

5. Contrastive Adversarial Domain Adaptation for Machine Remaining Useful Life Prediction

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