Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method-Reference-Cited by-同舟云学术

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Fault diagnosis of rolling bearings under variable conditions based on unsupervised domain adaptation method

Published:2024-06 Issue: Volume:215 Page:111430
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Zhong Jianhua,Lin Cong,Gao Yang,Zhong Jianfeng^ORCID,Zhong Shuncong

Publisher

Elsevier BV

Reference38 articles.

1. Fault diagnosis of rotating machinery based on multiple probabilistic classifiers;Zhong;Mech. Syst. Sig. Process.,2018

2. Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures;Wang;Mech. Syst. Sig. Process.,2022

3. Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples;Pan;ISA Trans.,2020

4. Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network;Wu;Comput. Ind.,2019

5. An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm;Li;Mech. Syst. Sig. Process.,2020

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