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