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Applied Sciences
Volume 14
Issue 11
10.3390/app14114515
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Open AccessArticle
by Xiaolei Pan SciProfilesScilitPreprints.orgGoogle Scholar Hongxiao Chen SciProfilesScilitPreprints.orgGoogle Scholar Dongdong Zhao SciProfilesScilitPreprints.orgGoogle Scholar Ao Shen SciProfilesScilitPreprints.orgGoogle Scholar Xiaoyan Su SciProfilesScilitPreprints.orgGoogle ScholarXiaolei Pan
,
Hongxiao Chen
,
Dongdong Zhao
Ao Shen
Xiaoyan Su
1
College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
2
Shanghai Key Laboratory of Power Station Automation Technology, Shanghai 200090, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(11), 4515; https://doi.org/10.3390/app14114515
Submission received: 18 April 2024/Revised: 21 May 2024/Accepted: 22 May 2024/Published: 24 May 2024
Abstract
Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.
Keywords: bearing fault diagnosis; transfer learning; graph convolution network; domain adaptation
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MDPI and ACS Style
Pan, X.; Chen, H.; Zhao, D.; Shen, A.; Su, X.Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method. Appl. Sci. 2024, 14, 4515.https://doi.org/10.3390/app14114515
AMA Style
Pan X, Chen H, Zhao D, Shen A, Su X.Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method. Applied Sciences. 2024; 14(11):4515.https://doi.org/10.3390/app14114515
Chicago/Turabian Style
Pan, Xiaolei, Hongxiao Chen, Dongdong Zhao, Ao Shen, and Xiaoyan Su.2024. "Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method" Applied Sciences 14, no. 11: 4515.https://doi.org/10.3390/app14114515
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Pan, X.; Chen, H.; Zhao, D.; Shen, A.; Su, X.Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method. Appl. Sci. 2024, 14, 4515.https://doi.org/10.3390/app14114515
AMA Style
Pan X, Chen H, Zhao D, Shen A, Su X.Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method. Applied Sciences. 2024; 14(11):4515.https://doi.org/10.3390/app14114515
Chicago/Turabian Style
Pan, Xiaolei, Hongxiao Chen, Dongdong Zhao, Ao Shen, and Xiaoyan Su.2024. "Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method" Applied Sciences 14, no. 11: 4515.https://doi.org/10.3390/app14114515
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Appl. Sci.,EISSN 2076-3417,Published by MDPI
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