Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation-Reference-Cited by-同舟云学术

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Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Published:2025-01 Issue: Volume:223 Page:111824
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Xie Zongliang,Chen Jinglong,Shi Zhen,Liu Shen,He Shuilong

Funder

Key Research and Development Projects of Shaanxi Province

Science and Technology Major Project of Guangxi

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference42 articles.

1. Deep network-based maximum correlated kurtosis deconvolution: a novel deep deconvolution for bearing fault diagnosis;Miao;Mech. Syst. Sig. Process.,2023

2. Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes;Qin;Mech. Syst. Sig. Process.,2021

3. Subspace Network with Shared Representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples;Liu;ISA Transactions,2021

4. Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing;Chen;Measurement,2019

5. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing;Lv;Mech. Syst. Sig. Process.,2016

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