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Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network
Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operato...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539661/ https://www.ncbi.nlm.nih.gov/pubmed/28677638 http://dx.doi.org/10.3390/s17071564 |
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author | He, Jun Yang, Shixi Gan, Chunbiao |
author_facet | He, Jun Yang, Shixi Gan, Chunbiao |
author_sort | He, Jun |
collection | PubMed |
description | Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. |
format | Online Article Text |
id | pubmed-5539661 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-55396612017-08-11 Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network He, Jun Yang, Shixi Gan, Chunbiao Sensors (Basel) Article Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods. MDPI 2017-07-04 /pmc/articles/PMC5539661/ /pubmed/28677638 http://dx.doi.org/10.3390/s17071564 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article He, Jun Yang, Shixi Gan, Chunbiao Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title | Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title_full | Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title_fullStr | Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title_full_unstemmed | Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title_short | Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network |
title_sort | unsupervised fault diagnosis of a gear transmission chain using a deep belief network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539661/ https://www.ncbi.nlm.nih.gov/pubmed/28677638 http://dx.doi.org/10.3390/s17071564 |
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