Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition-Reference-Cited by-同舟云学术

Attention on the key modes: Machinery fault diagnosis transformers through variational mode decomposition

Published:2024-04 Issue: Volume:289 Page:111479
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Liu Hebin,Xu Qizhi,Han Xiaolin,Wang Biao^ORCID,Yi Xiaojian

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference52 articles.

1. SKND-TSACNN: A novel time-scale adaptive CNN framework for fault diagnosis of rotating machinery;Yu;Knowl.-Based Syst.,2023

2. Towards trustworthy rotating machinery fault diagnosis via attention uncertainty in Transformer;Xiao;J. Manuf. Syst.,2023

3. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network;Cheng;Knowl.-Based Syst.,2021

4. A new gear intelligent fault diagnosis method based on refined composite hierarchical fluctuation dispersion entropy and manifold learning;Zhou;Measurement,2021

5. Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals;Tama;Artif. Intell. Rev.,2022

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