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Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network
At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification dif...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217265/ https://www.ncbi.nlm.nih.gov/pubmed/37238492 http://dx.doi.org/10.3390/e25050737 |
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author | Zhang, Xiong Li, Jialu Wu, Wenbo Dong, Fan Wan, Shuting |
author_facet | Zhang, Xiong Li, Jialu Wu, Wenbo Dong, Fan Wan, Shuting |
author_sort | Zhang, Xiong |
collection | PubMed |
description | At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed. The convolution neural network adopts a simple structure of three-layer convolution. The average pooling layer is used to replace the common maximum pooling layer, and the global average pooling layer is used to replace the full connection layer. The BN layer is used to optimize the model. The collected multi-class signals are used as the input of the model, and the improved convolution neural network is used for fault identification and classification of the input signals. The experimental data of XJTU-SY and Paderborn University show that the method proposed in this paper has a good effect on the multi-classification of bearing faults. |
format | Online Article Text |
id | pubmed-10217265 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102172652023-05-27 Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network Zhang, Xiong Li, Jialu Wu, Wenbo Dong, Fan Wan, Shuting Entropy (Basel) Article At present, the fault diagnosis methods for rolling bearings are all based on research with fewer fault categories, without considering the problem of multiple faults. In practical applications, the coexistence of multiple operating conditions and faults can lead to an increase in classification difficulty and a decrease in diagnostic accuracy. To solve this problem, a fault diagnosis method based on an improved convolution neural network is proposed. The convolution neural network adopts a simple structure of three-layer convolution. The average pooling layer is used to replace the common maximum pooling layer, and the global average pooling layer is used to replace the full connection layer. The BN layer is used to optimize the model. The collected multi-class signals are used as the input of the model, and the improved convolution neural network is used for fault identification and classification of the input signals. The experimental data of XJTU-SY and Paderborn University show that the method proposed in this paper has a good effect on the multi-classification of bearing faults. MDPI 2023-04-29 /pmc/articles/PMC10217265/ /pubmed/37238492 http://dx.doi.org/10.3390/e25050737 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Xiong Li, Jialu Wu, Wenbo Dong, Fan Wan, Shuting Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title | Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title_full | Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title_fullStr | Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title_full_unstemmed | Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title_short | Multi-Fault Classification and Diagnosis of Rolling Bearing Based on Improved Convolution Neural Network |
title_sort | multi-fault classification and diagnosis of rolling bearing based on improved convolution neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217265/ https://www.ncbi.nlm.nih.gov/pubmed/37238492 http://dx.doi.org/10.3390/e25050737 |
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