An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device

Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and...

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Autores principales:	Feng Ding, Yuan Xia, Jianhui Tian, Xinrui Zhang, Guangchu Hu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	AVMD DBN feature extraction fault identification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a349bf0c6f33449b8f77d2d17fdf7217
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spelling	oai:doaj.org-article:a349bf0c6f33449b8f77d2d17fdf72172021-11-18T00:01:57ZAn AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device2169-353610.1109/ACCESS.2021.3079237https://doaj.org/article/a349bf0c6f33449b8f77d2d17fdf72172021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9438048/https://doaj.org/toc/2169-3536Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and uses deep belief network (DBN) for pattern recognition. The vibration signal is decomposed by AVMD using energy ratio method. The intrinsic mode function (IMF) with abundant fault information is obtained. By calculating the energy entropy of each IMF component and form a high-dimensional feature vector as the input of DBN to establish an early fault identification model. The early fault data of the PHM2009 transmission device experimental platform was selected for identification and analysis. The identification results show that AVMD can extract the weak features of transmission device fault signals more accurately than empirical mode decomposition (EMD). Moreover, DBN has a higher fault identification accuracy rate than support vector machine (SVM), probabilistic neural network (PNN), back propagation neural network (BP) and Kohonen self-organizing competition neural network.Feng DingYuan XiaJianhui TianXinrui ZhangGuangchu HuIEEEarticleAVMDDBNfeature extractionfault identificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150088-150097 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AVMD DBN feature extraction fault identification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	AVMD DBN feature extraction fault identification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
description	Considering that the vibration signals of gears and bearings in the automatic transmission device are complex and the fault features are difficult to extract. This paper proposes a method for extracting fault features of transmission device using adaptive variational modal decomposition (AVMD), and uses deep belief network (DBN) for pattern recognition. The vibration signal is decomposed by AVMD using energy ratio method. The intrinsic mode function (IMF) with abundant fault information is obtained. By calculating the energy entropy of each IMF component and form a high-dimensional feature vector as the input of DBN to establish an early fault identification model. The early fault data of the PHM2009 transmission device experimental platform was selected for identification and analysis. The identification results show that AVMD can extract the weak features of transmission device fault signals more accurately than empirical mode decomposition (EMD). Moreover, DBN has a higher fault identification accuracy rate than support vector machine (SVM), probabilistic neural network (PNN), back propagation neural network (BP) and Kohonen self-organizing competition neural network.
format	article
author	Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu
author_facet	Feng Ding Yuan Xia Jianhui Tian Xinrui Zhang Guangchu Hu
author_sort	Feng Ding
title	An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
title_short	An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
title_full	An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
title_fullStr	An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
title_full_unstemmed	An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device
title_sort	avmd method based on energy ratio and deep belief network for fault identification of automation transmission device
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a349bf0c6f33449b8f77d2d17fdf7217
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_version_	1718425242704019456

An AVMD Method Based on Energy Ratio and Deep Belief Network for Fault Identification of Automation Transmission Device

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