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The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy

Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practica...

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Autores principales: Zhuang, Deyu, Liu, Hongrui, Zheng, Hao, Xu, Liang, Gu, Zhengyang, Cheng, Gang, Qiu, Jinbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863684/
https://www.ncbi.nlm.nih.gov/pubmed/36679788
http://dx.doi.org/10.3390/s23020991
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author Zhuang, Deyu
Liu, Hongrui
Zheng, Hao
Xu, Liang
Gu, Zhengyang
Cheng, Gang
Qiu, Jinbo
author_facet Zhuang, Deyu
Liu, Hongrui
Zheng, Hao
Xu, Liang
Gu, Zhengyang
Cheng, Gang
Qiu, Jinbo
author_sort Zhuang, Deyu
collection PubMed
description Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings.
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spelling pubmed-98636842023-01-22 The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy Zhuang, Deyu Liu, Hongrui Zheng, Hao Xu, Liang Gu, Zhengyang Cheng, Gang Qiu, Jinbo Sensors (Basel) Article Rolling bearings are important supporting components of large-scale electromechanical equipment. Once a fault occurs, it will cause economic losses, and serious accidents will affect personal safety. Therefore, research on rolling bearing fault diagnosis technology has important engineering practical significance. Feature extraction with high price density and fault identification are two keys to overcome in the field of fault diagnosis of rolling bearings. This study proposes a feature extraction method based on variational modal decomposition (VMD) and sample entropy and also designs an improved sequence minimization algorithm with optimal parameters to identify the fault. Firstly, a variational modal decomposition system based on vibration signals is designed, and the sample entropy of the components is extracted as the eigenvalue of the signal. Secondly, in order to improve the accuracy of fault diagnosis, the sequence minimum optimization algorithm optimized by the bat algorithm is used as the classifier. Certainly, the traditional bat algorithm (BA) and the sequence minimum optimization algorithm (SMO) are improved, respectively. Therefore, a fault diagnosis algorithm based on IBA-ISMO is obtained. Finally, the experimental verification is designed to prove that the algorithm model has a good state recognition rate for bearings. MDPI 2023-01-15 /pmc/articles/PMC9863684/ /pubmed/36679788 http://dx.doi.org/10.3390/s23020991 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
Zhuang, Deyu
Liu, Hongrui
Zheng, Hao
Xu, Liang
Gu, Zhengyang
Cheng, Gang
Qiu, Jinbo
The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title_full The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title_fullStr The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title_full_unstemmed The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title_short The IBA-ISMO Method for Rolling Bearing Fault Diagnosis Based on VMD-Sample Entropy
title_sort iba-ismo method for rolling bearing fault diagnosis based on vmd-sample entropy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863684/
https://www.ncbi.nlm.nih.gov/pubmed/36679788
http://dx.doi.org/10.3390/s23020991
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