Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machinery-Reference-Cited by-同舟云学术

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Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machinery

Published:2024-09 Issue: Volume:249 Page:123764
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Guo Baosu^ORCID,Qiao Zhaohui^ORCID,Zhang Ning,Wang Yongchun,Wu Fenghe,Peng Qingjin^ORCID

Publisher

Elsevier BV

Reference33 articles.

1. Domain adaptation network base on contrastive learning for bearings fault diagnosis under variable working conditions;An;Expert Systems with Applications,2023

2. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals;Ben Ali;Applied Acoustics,2015

3. An anti-noise fault diagnosis approach for rolling bearings based on multiscale CNN-LSTM and a deep residual learning model;Chen;Measurement Science and Technology,2023

4. Fault diagnosis of rolling Bearings based on an improved stack autoencoder and support vector machine;Cui;IEEE Sensors Journal,2021

5. A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm;Deng;Soft Computing,2019

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gear fault diagnosis based on small channel convolutional neural network under multiscale fusion attention mechanism;Quality and Reliability Engineering International;2024-08-06

2. ResNet diagnosis of rotor faults in oil transfer pumps;Heliyon;2024-08

3. Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM;Applied Sciences;2024-07-04

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