Cargando…

Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm

To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibr...

Descripción completa

Detalles Bibliográficos
Autores principales: Cao, Qingjiao, Wang, Liying, Zhao, Weiguo, Yuan, Zhouxiang, Liu, Anran, Gao, Yanfeng, Ye, Runfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296161/
https://www.ncbi.nlm.nih.gov/pubmed/37366838
http://dx.doi.org/10.3390/biomimetics8020243
_version_ 1785063592409694208
author Cao, Qingjiao
Wang, Liying
Zhao, Weiguo
Yuan, Zhouxiang
Liu, Anran
Gao, Yanfeng
Ye, Runfeng
author_facet Cao, Qingjiao
Wang, Liying
Zhao, Weiguo
Yuan, Zhouxiang
Liu, Anran
Gao, Yanfeng
Ye, Runfeng
author_sort Cao, Qingjiao
collection PubMed
description To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units.
format Online
Article
Text
id pubmed-10296161
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-102961612023-06-28 Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm Cao, Qingjiao Wang, Liying Zhao, Weiguo Yuan, Zhouxiang Liu, Anran Gao, Yanfeng Ye, Runfeng Biomimetics (Basel) Article To improve the identification accuracy of the vibration states of hydraulic units, an improved artificial rabbits optimization algorithm (IARO) adopting an adaptive weight adjustment strategy is developed for optimizing the support vector machine (SVM) to obtain an identification model, and the vibration signals with different states are classified and identified. The variational mode decomposition (VMD) method is used to decompose the vibration signals, and the multi-dimensional time-domain feature vectors of the signals are extracted. The IARO algorithm is used to optimize the parameters of the SVM multi-classifier. The multi-dimensional time-domain feature vectors are input into the IARO-SVM model to realize the classification and identification of vibration signal states, and the results are compared with those of the ARO-SVM model, ASO-SVM model, PSO-SVM model and WOA-SVM model. The comparative results show that the average identification accuracy of the IARO-SVM model is higher at 97.78% than its competitors, which is 3.34% higher than the closest ARO-SVM model. Therefore, the IARO-SVM model has higher identification accuracy and better stability, and can accurately identify the vibration states of hydraulic units. The research can provide a theoretical basis for the vibration identification of hydraulic units. MDPI 2023-06-08 /pmc/articles/PMC10296161/ /pubmed/37366838 http://dx.doi.org/10.3390/biomimetics8020243 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
Cao, Qingjiao
Wang, Liying
Zhao, Weiguo
Yuan, Zhouxiang
Liu, Anran
Gao, Yanfeng
Ye, Runfeng
Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title_full Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title_fullStr Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title_full_unstemmed Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title_short Vibration State Identification of Hydraulic Units Based on Improved Artificial Rabbits Optimization Algorithm
title_sort vibration state identification of hydraulic units based on improved artificial rabbits optimization algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10296161/
https://www.ncbi.nlm.nih.gov/pubmed/37366838
http://dx.doi.org/10.3390/biomimetics8020243
work_keys_str_mv AT caoqingjiao vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT wangliying vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT zhaoweiguo vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT yuanzhouxiang vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT liuanran vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT gaoyanfeng vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm
AT yerunfeng vibrationstateidentificationofhydraulicunitsbasedonimprovedartificialrabbitsoptimizationalgorithm