Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network
Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, comple...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/13/10/184 |
| _version_ | 1797469572973985792 |
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| author | Guangxin Li Yong Chen Wenqing Wang Yimin Wu Rui Liu |
| author_facet | Guangxin Li Yong Chen Wenqing Wang Yimin Wu Rui Liu |
| author_sort | Guangxin Li |
| collection | DOAJ |
| description | Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, complete ensemble empirical mode decomposition with adaptive noise independent component analysis (CEEMDANICA) and two-dimensional convolutional neural network (TDCNN) is proposed. Firstly, the original vibration signal of the bearing is preprocessed by CEEMDANICA, and the ICA components with different frequencies are obtained. Secondly, the ICA components are selected as the sample set by using multiscale permutation entropy, correlation coefficient, kurtosis and box dimension. Finally, the sample set are trained and tested by a DCNN model to realize the fault diagnosis of different bearing fault types. In order to verify the reliability of the method, a bearing fault vibration monitoring platform for an electric vehicle two-speed automatic transmission was built to collect the bearing vibration signals of multiple fault types under different working conditions. The diagnostic accuracy of several deep learning models is compared. The results show that the proposed method can realize the single and compound fault diagnosis of rolling-element bearings in an automatic transmission, with a high degree of accuracy. |
| first_indexed | 2024-03-09T19:23:15Z |
| format | Article |
| id | doaj.art-be32ac25b99c476f91e1e92cc0565251 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-09T19:23:15Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-be32ac25b99c476f91e1e92cc05652512023-11-24T03:14:13ZengMDPI AGWorld Electric Vehicle Journal2032-66532022-10-01131018410.3390/wevj13100184Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural NetworkGuangxin Li0Yong Chen1Wenqing Wang2Yimin Wu3Rui Liu4Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaTianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaWeichai Power Co., Ltd., Weifang 261061, ChinaTianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaTianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, ChinaRolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, complete ensemble empirical mode decomposition with adaptive noise independent component analysis (CEEMDANICA) and two-dimensional convolutional neural network (TDCNN) is proposed. Firstly, the original vibration signal of the bearing is preprocessed by CEEMDANICA, and the ICA components with different frequencies are obtained. Secondly, the ICA components are selected as the sample set by using multiscale permutation entropy, correlation coefficient, kurtosis and box dimension. Finally, the sample set are trained and tested by a DCNN model to realize the fault diagnosis of different bearing fault types. In order to verify the reliability of the method, a bearing fault vibration monitoring platform for an electric vehicle two-speed automatic transmission was built to collect the bearing vibration signals of multiple fault types under different working conditions. The diagnostic accuracy of several deep learning models is compared. The results show that the proposed method can realize the single and compound fault diagnosis of rolling-element bearings in an automatic transmission, with a high degree of accuracy.https://www.mdpi.com/2032-6653/13/10/184rolling-element bearingcomplete ensemble empirical mode decomposition with adaptive noiseindependent component analysisconvolutional neural networkfault diagnosis |
| spellingShingle | Guangxin Li Yong Chen Wenqing Wang Yimin Wu Rui Liu Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network World Electric Vehicle Journal rolling-element bearing complete ensemble empirical mode decomposition with adaptive noise independent component analysis convolutional neural network fault diagnosis |
| title | Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network |
| title_full | Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network |
| title_fullStr | Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network |
| title_full_unstemmed | Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network |
| title_short | Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network |
| title_sort | automatic transmission bearing fault diagnosis based on comprehensive index method and convolutional neural network |
| topic | rolling-element bearing complete ensemble empirical mode decomposition with adaptive noise independent component analysis convolutional neural network fault diagnosis |
| url | https://www.mdpi.com/2032-6653/13/10/184 |
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