Dynamic Bayesian Networks for Feature Learning and Transfer Applications in Remaining Useful Life Estimation-Reference-Cited by-同舟云学术

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Dynamic Bayesian Networks for Feature Learning and Transfer Applications in Remaining Useful Life Estimation

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zeng Lingquan¹^ORCID,Zheng Junhua²^ORCID,Yao Le³^ORCID,Ge Zhiqiang¹^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China

3. School of Mathematics, Hangzhou Normal University, Hangzhou, China

Funder

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/ielx7/19/10012124/09950365.pdf?arnumber=9950365

Reference44 articles.

1. Direct Remaining Useful Life Estimation Based on Support Vector Regression

2. Remaining useful life estimation using deep metric transfer learning for kernel regression

3. An adaptive remaining useful life prediction approach for single battery with unlabeled small sample data and parameter uncertainty

4. A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

5. Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

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