An Interpretable Deep Transfer Learning-Based Remaining Useful Life Prediction Approach for Bearings With Selective Degradation Knowledge Fusion-Reference-Cited by-同舟云学术

An Interpretable Deep Transfer Learning-Based Remaining Useful Life Prediction Approach for Bearings With Selective Degradation Knowledge Fusion

Author:

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

Natural Science and Engineering Research Council of Canada

University Research Grants Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

Reference41 articles.

2. Correcting sample selection bias by unlabeled data;huang;Proc Adv Neural Inf Process Syst,2007

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