An Adaptive Sparse Graph Learning Method Based on Digital Twin Dictionary for Remaining Useful Life Prediction of Rolling Element Bearings-Reference-Cited by-同舟云学术

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An Adaptive Sparse Graph Learning Method Based on Digital Twin Dictionary for Remaining Useful Life Prediction of Rolling Element Bearings

Published:2024-09 Issue:9 Volume:20 Page:10892-10900
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Cui Lingli¹^ORCID,Wang Xin²^ORCID,Liu Dongdong¹^ORCID,Wang Huaqing³^ORCID

Affiliation:

1. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, China

2. School of Aeronautics, Northwestern Polytechnical University, Xi'an, China

3. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10534290.pdf?arnumber=10534290

Reference35 articles.

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

2. A Projective and Discriminative Dictionary Learning for High-Dimensional Process Monitoring With Industrial Applications

3. Fault diagnosis of offshore wind turbines based on component separable synchroextracting transform

4. An adaptive and generalized Wiener process model with a recursive filtering algorithm for remaining useful life estimation

5. New fusion and selection approaches for estimating the remaining useful life using Gaussian process regression and induced ordered weighted averaging operators

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