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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/11/1589 |
| _version_ | 1797468370645286912 |
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| author | Zhibo Li Yuanyuan Li Qichun Sun Bowei Qi |
| author_facet | Zhibo Li Yuanyuan Li Qichun Sun Bowei Qi |
| author_sort | Zhibo Li |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T19:05:31Z |
| format | Article |
| id | doaj.art-7801c9e30e7a45b4899434f69447fdff |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T19:05:31Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
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| spelling | doaj.art-7801c9e30e7a45b4899434f69447fdff2023-11-24T04:36:43ZengMDPI AGEntropy1099-43002022-11-012411158910.3390/e24111589Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge GraphZhibo Li0Yuanyuan Li1Qichun Sun2Bowei Qi3School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaAn 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.https://www.mdpi.com/1099-4300/24/11/1589fault diagnosisconvolutional neural networkknowledge graphattention mechanism |
| spellingShingle | Zhibo Li Yuanyuan Li Qichun Sun Bowei Qi Bearing Fault Diagnosis Method Based on Convolutional Neural Network and Knowledge Graph Entropy fault diagnosis convolutional neural network knowledge graph attention mechanism |
| 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 | fault diagnosis convolutional neural network knowledge graph attention mechanism |
| url | https://www.mdpi.com/1099-4300/24/11/1589 |
| work_keys_str_mv | AT zhiboli bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT yuanyuanli bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT qichunsun bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph AT boweiqi bearingfaultdiagnosismethodbasedonconvolutionalneuralnetworkandknowledgegraph |