Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions-Reference-Cited by-同舟云学术

Global contextual feature aggregation networks with multiscale attention mechanism for mechanical fault diagnosis under non-stationary conditions

Published:2023-11 Issue: Volume:203 Page:110724
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Xu Yadong^ORCID,Chen Yuejian,Zhang Hengcheng^ORCID,Feng Ke,Wang Yulin,Yang Chunsheng,Ni Qing

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference37 articles.

1. A graph-guided collaborative convolutional neural network for fault diagnosis of electromechanical systems;Xu;Mech. Syst. Signal Process.,2023

2. Cross-modal fusion convolutional neural networks with online soft label training strategy for mechanical fault diagnosis;Xu;IEEE Trans. Ind. Inform.,2023

3. Multireceptive field denoising residual convolutional networks for fault diagnosis;Xu;IEEE Trans. Ind. Electron.,2021

4. Deep convolutional generative adversarial network with semi-supervised learning enabled physics elucidation for extended gear fault diagnosis under data limitations;Zhou;Mech. Syst. Signal Process.,2023

5. TDMSAE: A transferable decoupling multi-scale autoencoder for mechanical fault diagnosis;Yu;Mech. Syst. Signal Process.,2023

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