Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM
Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/1205473 |
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| _version_ | 1832560716363595776 |
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| author | Yanwei Xu Weiwei Cai Liuyang Wang Tancheng Xie |
| author_facet | Yanwei Xu Weiwei Cai Liuyang Wang Tancheng Xie |
| author_sort | Yanwei Xu |
| collection | DOAJ |
| description | Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models. |
| format | Article |
| id | doaj-art-1743c6fbe7b94f34966c587caec2e3ef |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-1743c6fbe7b94f34966c587caec2e3ef2025-02-03T01:26:55ZengWileyShock and Vibration1875-92032021-01-01202110.1155/2021/1205473Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBMYanwei Xu0Weiwei Cai1Liuyang Wang2Tancheng Xie3School of Mechatronics EngineeringSchool of Mechatronics EngineeringSchool of Mechatronics EngineeringSchool of Mechatronics EngineeringAiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models.http://dx.doi.org/10.1155/2021/1205473 |
| spellingShingle | Yanwei Xu Weiwei Cai Liuyang Wang Tancheng Xie Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM Shock and Vibration |
| title | Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM |
| title_full | Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM |
| title_fullStr | Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM |
| title_full_unstemmed | Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM |
| title_short | Intelligent Diagnosis of Rolling Bearing Fault Based on Improved Convolutional Neural Network and LightGBM |
| title_sort | intelligent diagnosis of rolling bearing fault based on improved convolutional neural network and lightgbm |
| url | http://dx.doi.org/10.1155/2021/1205473 |
| work_keys_str_mv | AT yanweixu intelligentdiagnosisofrollingbearingfaultbasedonimprovedconvolutionalneuralnetworkandlightgbm AT weiweicai intelligentdiagnosisofrollingbearingfaultbasedonimprovedconvolutionalneuralnetworkandlightgbm AT liuyangwang intelligentdiagnosisofrollingbearingfaultbasedonimprovedconvolutionalneuralnetworkandlightgbm AT tanchengxie intelligentdiagnosisofrollingbearingfaultbasedonimprovedconvolutionalneuralnetworkandlightgbm |