Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function
In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration si...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7593 |
| _version_ | 1797592135890894848 |
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| author | Haihua Qin Jiafang Pan Jian Li Faguo Huang |
| author_facet | Haihua Qin Jiafang Pan Jian Li Faguo Huang |
| author_sort | Haihua Qin |
| collection | DOAJ |
| description | In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value. |
| first_indexed | 2024-03-11T01:47:10Z |
| format | Article |
| id | doaj.art-821fb7ff45f548ed938c528ffd5175b5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:10Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-821fb7ff45f548ed938c528ffd5175b52023-11-18T16:08:38ZengMDPI AGApplied Sciences2076-34172023-06-011313759310.3390/app13137593Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation FunctionHaihua Qin0Jiafang Pan1Jian Li2Faguo Huang3Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaKey Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin 541006, ChinaIn order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value.https://www.mdpi.com/2076-3417/13/13/7593bearing fault diagnosisattention mechanismresidual networkACON activation functiondeep learning |
| spellingShingle | Haihua Qin Jiafang Pan Jian Li Faguo Huang Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function Applied Sciences bearing fault diagnosis attention mechanism residual network ACON activation function deep learning |
| title | Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function |
| title_full | Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function |
| title_fullStr | Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function |
| title_full_unstemmed | Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function |
| title_short | Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function |
| title_sort | fault diagnosis method of rolling bearing based on cbam resnet and acon activation function |
| topic | bearing fault diagnosis attention mechanism residual network ACON activation function deep learning |
| url | https://www.mdpi.com/2076-3417/13/13/7593 |
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