MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network
Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5532 |
| _version_ | 1797592702324310016 |
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| author | Feiqing Zhang Zhenyu Yin Fulong Xu Yue Li Guangyuan Xu |
| author_facet | Feiqing Zhang Zhenyu Yin Fulong Xu Yue Li Guangyuan Xu |
| author_sort | Feiqing Zhang |
| collection | DOAJ |
| description | Rolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises. To address the above problems, this paper proposes an improved dilated-convolutional-neural network-based bearing fault diagnosis method in noisy environments named MAB-DrNet. First, a basic model called the dilated residual network (DrNet) was designed based on the residual block to enlarge the model’s perceptual field to better capture the features from bearing fault signals. Then, a max-average block (MAB) module was designed to improve the feature extraction capability of the model. In addition, the global residual block (GRB) module was introduced into MAB-DrNet to further improve the performance of the proposed model, enabling the model to better handle the global information of the input data and improve the classification accuracy of the model in noisy environments. Finally, the proposed method was tested on the CWRU dataset, and the results showed that the proposed method had good noise immunity; the accuracy was 95.57% when adding Gaussian white noises with a signal-to-noise ratio of −6 dB. The proposed method was also compared with existing advanced methods to further prove its high accuracy. |
| first_indexed | 2024-03-11T01:56:52Z |
| format | Article |
| id | doaj.art-45754db10bbf4da38bba056147238c96 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:52Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-45754db10bbf4da38bba056147238c962023-11-18T12:32:26ZengMDPI AGSensors1424-82202023-06-012312553210.3390/s23125532MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural NetworkFeiqing Zhang0Zhenyu Yin1Fulong Xu2Yue Li3Guangyuan Xu4Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaShenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, ChinaRolling bearing fault diagnosis is of great significance to the safe and reliable operation of manufacturing equipment. In the actual complex environment, the collected bearing signals usually contain a large amount of noises from the resonances of the environment and other components, resulting in the nonlinear characteristics of the collected data. Existing deep-learning-based solutions for bearing fault diagnosis perform poorly in classification performance under noises. To address the above problems, this paper proposes an improved dilated-convolutional-neural network-based bearing fault diagnosis method in noisy environments named MAB-DrNet. First, a basic model called the dilated residual network (DrNet) was designed based on the residual block to enlarge the model’s perceptual field to better capture the features from bearing fault signals. Then, a max-average block (MAB) module was designed to improve the feature extraction capability of the model. In addition, the global residual block (GRB) module was introduced into MAB-DrNet to further improve the performance of the proposed model, enabling the model to better handle the global information of the input data and improve the classification accuracy of the model in noisy environments. Finally, the proposed method was tested on the CWRU dataset, and the results showed that the proposed method had good noise immunity; the accuracy was 95.57% when adding Gaussian white noises with a signal-to-noise ratio of −6 dB. The proposed method was also compared with existing advanced methods to further prove its high accuracy.https://www.mdpi.com/1424-8220/23/12/5532fault diagnosisdilated convolutiondeep learningresidual networknoisy environment |
| spellingShingle | Feiqing Zhang Zhenyu Yin Fulong Xu Yue Li Guangyuan Xu MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network Sensors fault diagnosis dilated convolution deep learning residual network noisy environment |
| title | MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network |
| title_full | MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network |
| title_fullStr | MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network |
| title_full_unstemmed | MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network |
| title_short | MAB-DrNet: Bearing Fault Diagnosis Method Based on an Improved Dilated Convolutional Neural Network |
| title_sort | mab drnet bearing fault diagnosis method based on an improved dilated convolutional neural network |
| topic | fault diagnosis dilated convolution deep learning residual network noisy environment |
| url | https://www.mdpi.com/1424-8220/23/12/5532 |
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