Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings-Reference-Cited by-同舟云学术

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Heterogeneous graph representation-driven multiplex aggregation graph neural network for remaining useful life prediction of bearings

Published:2024-11 Issue: Volume:220 Page:111679
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Xiao Yongchang^ORCID,Liu Dongdong,Cui Lingli^ORCID,Wang Huaqing^ORCID

Funder

Beijing Municipal Natural Science Foundation

Natural Science Foundation of Beijing Municipality

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference48 articles.

1. Adaptive fault components extraction by using an optimized weights spectrum based index for machinery fault diagnosis;Hou;IEEE Trans. Ind. Electron.,2024

2. An optimal-subdomain generalization method for remaining useful life prediction of machinery under time-varying operation conditions;Liu;IEEE Trans. Ind. Inf.,2023

3. Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group;Han;Mech. Syst. Sig. Process.,2023

4. Normalized conditional variational auto-encoder with adaptive focal loss for imbalanced fault diagnosis of bearing-rotor system;Zhao;Mech. Syst. Sig. Process.,2022

5. A novel robust dual unscented particle filter method for remaining useful life prediction of rolling bearings;Cui;IEEE Trans. Instrum. Meas.,2024

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