Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes a...

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Autores principales:	Hong Tang, Zhengxing Yuan, Hongliang Dai, Yi Du
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Probability box theory feature vector support vector machine genetic algorithm fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/dcdde715913a4975990c9ccf80a90ee0
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spelling	oai:doaj.org-article:dcdde715913a4975990c9ccf80a90ee02021-11-19T00:05:45ZFault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM2169-353610.1109/ACCESS.2020.3024792https://doaj.org/article/dcdde715913a4975990c9ccf80a90ee02020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9199902/https://doaj.org/toc/2169-3536For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.Hong TangZhengxing YuanHongliang DaiYi DuIEEEarticleProbability box theoryfeature vectorsupport vector machinegenetic algorithmfault diagnosisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 170872-170882 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Probability box theory feature vector support vector machine genetic algorithm fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Probability box theory feature vector support vector machine genetic algorithm fault diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hong Tang Zhengxing Yuan Hongliang Dai Yi Du Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
description	For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method.
format	article
author	Hong Tang Zhengxing Yuan Hongliang Dai Yi Du
author_facet	Hong Tang Zhengxing Yuan Hongliang Dai Yi Du
author_sort	Hong Tang
title	Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
title_short	Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
title_full	Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
title_fullStr	Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
title_full_unstemmed	Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM
title_sort	fault diagnosis of rolling bearing based on probability box theory and ga-svm
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/dcdde715913a4975990c9ccf80a90ee0
work_keys_str_mv	AT hongtang faultdiagnosisofrollingbearingbasedonprobabilityboxtheoryandgasvm AT zhengxingyuan faultdiagnosisofrollingbearingbasedonprobabilityboxtheoryandgasvm AT hongliangdai faultdiagnosisofrollingbearingbasedonprobabilityboxtheoryandgasvm AT yidu faultdiagnosisofrollingbearingbasedonprobabilityboxtheoryandgasvm
_version_	1718420699104673792

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

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