A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and class...

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Autores principales:	Bin Han, Hui Zhang, Ming Sun, Fengtong Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intelligent fault diagnosis time series classification convolutional neural networks Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/c109bc54af76403fa12886d990ca753b
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spelling	oai:doaj.org-article:c109bc54af76403fa12886d990ca753b2021-11-25T18:59:07ZA New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field10.3390/s212277621424-8220https://doaj.org/article/c109bc54af76403fa12886d990ca753b2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7762https://doaj.org/toc/1424-8220Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.Bin HanHui ZhangMing SunFengtong WuMDPI AGarticleintelligent fault diagnosistime series classificationconvolutional neural networksChemical technologyTP1-1185ENSensors, Vol 21, Iss 7762, p 7762 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent fault diagnosis time series classification convolutional neural networks Chemical technology TP1-1185
spellingShingle	intelligent fault diagnosis time series classification convolutional neural networks Chemical technology TP1-1185 Bin Han Hui Zhang Ming Sun Fengtong Wu A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
description	Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.
format	article
author	Bin Han Hui Zhang Ming Sun Fengtong Wu
author_facet	Bin Han Hui Zhang Ming Sun Fengtong Wu
author_sort	Bin Han
title	A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
title_short	A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
title_full	A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
title_fullStr	A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
title_full_unstemmed	A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
title_sort	new bearing fault diagnosis method based on capsule network and markov transition field/gramian angular field
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c109bc54af76403fa12886d990ca753b
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A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

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