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Multi-layer neural network with deep belief network for gearbox fault diagnosis

Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based l...

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Autor Principal: Li, Chuan
Formato: Artículos
Lenguaje:eng
Publicado: 2016
Materias:
MULTI-LAYER
NEURAL NETWORK
BELIEF NETWORK
GEARBOX
Acceso en línea:http://repositorio.educacionsuperior.gob.ec/handle/28000/2982
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spelling oai:localhost:28000-29822017-04-12T15:40:43Z Multi-layer neural network with deep belief network for gearbox fault diagnosis Li, Chuan MULTI-LAYER NEURAL NETWORK BELIEF NETWORK GEARBOX Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. Universidad Polit?cnica Salesiana http://connection.ebscohost.com/c/articles/109000172/multi-layer-neural-network-deep-belief-network-gearbox-fault-diagnosis 2016-11-09T16:16:24Z 2016-11-09T16:16:24Z 2015 article Chen, Zhiqiang; Li, Chuan; S?nchez, Ren?-Vinicio. (2015). Multi-layer neural network with deep belief network for gearbox fault diagnosis. Journal of Vibroengineering. Aug2015, Vol. 17 Issue 5, p2379 1392-8716 http://repositorio.educacionsuperior.gob.ec/handle/28000/2982 eng restrictedAccess p. 2379
institution SENESCYT
collection Repositorio SENESCYT
biblioteca Biblioteca Senescyt
language eng
format Artículos
topic MULTI-LAYER
NEURAL NETWORK
BELIEF NETWORK
GEARBOX
spellingShingle MULTI-LAYER
NEURAL NETWORK
BELIEF NETWORK
GEARBOX
Li, Chuan
Multi-layer neural network with deep belief network for gearbox fault diagnosis
description Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery.
author Li, Chuan
author_facet Li, Chuan
author_sort Li, Chuan
title Multi-layer neural network with deep belief network for gearbox fault diagnosis
title_short Multi-layer neural network with deep belief network for gearbox fault diagnosis
title_full Multi-layer neural network with deep belief network for gearbox fault diagnosis
title_fullStr Multi-layer neural network with deep belief network for gearbox fault diagnosis
title_full_unstemmed Multi-layer neural network with deep belief network for gearbox fault diagnosis
title_sort multi-layer neural network with deep belief network for gearbox fault diagnosis
publishDate 2016
url http://repositorio.educacionsuperior.gob.ec/handle/28000/2982
_version_ 1634995098090471424
score 11,871979

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