Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network-Reference-Cited by-同舟云学术

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Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network

Published:2024-07 Issue: Volume:133 Page:108087
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Zheng Xiaoyang,Yang Peixi^ORCID,Yan Kai,He Yunze,Yu Qianjiang,Li Mingyan

Publisher

Elsevier BV

Reference41 articles.

1. Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning;Che;Digit. Signal Process.,2022

2. Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis;Chen;Control Eng. Pract.,2021

3. Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis;Chen;Mech. Syst. Signal Process.,2023

4. Multiwavelet analysis and signal processing;Cotronei;IEEE Trans. Circuits Syst. II,1998

5. Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes;Elshenawy;J. Franklin Inst. B,2022

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