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Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VM...

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Detalles Bibliográficos
Autores principales: Jin, Zhihao, Chen, Guangdong, Yang, Zhengxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319513/
https://www.ncbi.nlm.nih.gov/pubmed/35885150
http://dx.doi.org/10.3390/e24070927
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author Jin, Zhihao
Chen, Guangdong
Yang, Zhengxin
author_facet Jin, Zhihao
Chen, Guangdong
Yang, Zhengxin
author_sort Jin, Zhihao
collection PubMed
description In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.
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spelling pubmed-93195132022-07-27 Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM Jin, Zhihao Chen, Guangdong Yang, Zhengxin Entropy (Basel) Article In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features. MDPI 2022-07-03 /pmc/articles/PMC9319513/ /pubmed/35885150 http://dx.doi.org/10.3390/e24070927 Text en © 2022 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
Jin, Zhihao
Chen, Guangdong
Yang, Zhengxin
Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title_full Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title_fullStr Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title_full_unstemmed Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title_short Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM
title_sort rolling bearing fault diagnosis based on woa-vmd-mpe and mpso-lssvm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319513/
https://www.ncbi.nlm.nih.gov/pubmed/35885150
http://dx.doi.org/10.3390/e24070927
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