Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper propo...

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Autores principales:	Shu Han, Xiaoming Liu, Yan Yang, Hailin Cao, Yuanhong Zhong, Chuanlian Luo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	mechanical composite fault feature separation VMD MAP Technology T
Acceso en línea:	https://doaj.org/article/efc0bb6fb0a44c0f8b3be222af63f6c3
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spelling	oai:doaj.org-article:efc0bb6fb0a44c0f8b3be222af63f6c32021-11-25T17:28:03ZIntelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals10.3390/en142277021996-1073https://doaj.org/article/efc0bb6fb0a44c0f8b3be222af63f6c32021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7702https://doaj.org/toc/1996-1073With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.Shu HanXiaoming LiuYan YangHailin CaoYuanhong ZhongChuanlian LuoMDPI AGarticlemechanical composite faultfeature separationVMDMAPTechnologyTENEnergies, Vol 14, Iss 7702, p 7702 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	mechanical composite fault feature separation VMD MAP Technology T
spellingShingle	mechanical composite fault feature separation VMD MAP Technology T Shu Han Xiaoming Liu Yan Yang Hailin Cao Yuanhong Zhong Chuanlian Luo Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
description	With the development of modern industry and scientific technology, production equipment plays an increasingly important role in military and industrial production, and the fault detection signal of gears and bearings state in transmission equipment becomes very important. Therefore, this paper proposes a gear-bearing composite fault signal decomposition and reconstruction method, which combines the marine predator algorithm (MPA) and variational mode decomposition (VMD) technologies. For the parameters’ selection of VMD, the optimization algorithm allows us to quickly and accurately obtain the results with the best kurtosis correlation index after signal decomposition and reconstruction. The experiments demonstrate the excellent performance of our method in the field of separation and denoising mixed gear-bearing fault signals.
format	article
author	Shu Han Xiaoming Liu Yan Yang Hailin Cao Yuanhong Zhong Chuanlian Luo
author_facet	Shu Han Xiaoming Liu Yan Yang Hailin Cao Yuanhong Zhong Chuanlian Luo
author_sort	Shu Han
title	Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
title_short	Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
title_full	Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
title_fullStr	Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
title_full_unstemmed	Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals
title_sort	intelligent algorithm for variable scale adaptive feature separation of mechanical composite fault signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/efc0bb6fb0a44c0f8b3be222af63f6c3
work_keys_str_mv	AT shuhan intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals AT xiaomingliu intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals AT yanyang intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals AT hailincao intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals AT yuanhongzhong intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals AT chuanlianluo intelligentalgorithmforvariablescaleadaptivefeatureseparationofmechanicalcompositefaultsignals
_version_	1718412320621723648

Intelligent Algorithm for Variable Scale Adaptive Feature Separation of Mechanical Composite Fault Signals

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