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Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph
An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfu...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689069/ https://www.ncbi.nlm.nih.gov/pubmed/36359679 http://dx.doi.org/10.3390/e24111589 |
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author | Li, Zhibo Li, Yuanyuan Sun, Qichun Qi, Bowei |
author_facet | Li, Zhibo Li, Yuanyuan Sun, Qichun Qi, Bowei |
author_sort | Li, Zhibo |
collection | PubMed |
description | An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use. |
format | Online Article Text |
id | pubmed-9689069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96890692022-11-25 Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph Li, Zhibo Li, Yuanyuan Sun, Qichun Qi, Bowei Entropy (Basel) Article An effective fault diagnosis method of bearing is the key to predictive maintenance of modern industrial equipment. With the single use of equipment failure mechanism or operation of data, it is hard to resolve multiple complex variable working conditions, multiple types of fault and equipment malfunctions and failures related to knowledge and data. In order to solve these problems, a fault diagnosis method based on the fusion of deep learning with a knowledge graph is proposed in this paper. Firstly, the knowledge rules of bearing data is used for entity extraction. Next, the multiscale optimized convolutional neural network (MOCNN) proposed in this paper is used for fault classification to achieve relationship extraction. Finally, the fault diagnosis graph of the bearing is constructed for fault-assisted decision-making as well as the detailed display of fault information. According to experiment analysis, the fault diagnosis model based on MOCNN proposed in this paper, which integrates the end-to-end convolutional neural network and the attention mechanism, still achieves an accuracy of 97.86% under the data set of 160 types of faults. Compared with the deep learning models such as Resnet and Inception in the noise environment of multiple working conditions and variable working conditions, the model proposed in this paper not only shows a faster convergence speed and stable performance, but also a higher accuracy in evaluation indicators, which is beneficial to practical use. MDPI 2022-11-02 /pmc/articles/PMC9689069/ /pubmed/36359679 http://dx.doi.org/10.3390/e24111589 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Zhibo Li, Yuanyuan Sun, Qichun Qi, Bowei Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title | Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title_full | Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title_fullStr | Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title_full_unstemmed | Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title_short | Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph |
title_sort | bearing fault diagnosis method based on convolutional neural network and knowledge graph |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689069/ https://www.ncbi.nlm.nih.gov/pubmed/36359679 http://dx.doi.org/10.3390/e24111589 |
work_keys_str_mv | AT lizhibo bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT liyuanyuan bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT sunqichun bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT qibowei bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph |