Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN
Many components of electric vehicles contain rolling bearings, and the operating condition of rolling bearings often affects the operating performance of electric vehicles. Monitoring the operating status of the bearings is one of the key technologies to ensure the safe operation of the bearings. We...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/13/11/208 |
| _version_ | 1797466171429093376 |
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| author | Jianguo Yin Gang Cen |
| author_facet | Jianguo Yin Gang Cen |
| author_sort | Jianguo Yin |
| collection | DOAJ |
| description | Many components of electric vehicles contain rolling bearings, and the operating condition of rolling bearings often affects the operating performance of electric vehicles. Monitoring the operating status of the bearings is one of the key technologies to ensure the safe operation of the bearings. We propose a channel attention-based convolutional neural network (CA-CNN) model for rolling bearing fault diagnosis. The model can directly use the raw vibration signal of the bearing as input to achieve bearing fault diagnosis under different operating loads and different noise environments. The experimental results show that, compared with other intelligent diagnosis methods, the proposed model CA-CNN achieves a high diagnostic accuracy under different load cases and still has advantages in different noisy environments. It is also beneficial to promote the intelligent fault diagnosis and maintenance of electric vehicles. |
| first_indexed | 2024-03-09T18:33:00Z |
| format | Article |
| id | doaj.art-3af355138ff04a9997a763fac7aa2b9f |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-09T18:33:00Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-3af355138ff04a9997a763fac7aa2b9f2023-11-24T07:22:06ZengMDPI AGWorld Electric Vehicle Journal2032-66532022-11-01131120810.3390/wevj13110208Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNNJianguo Yin0Gang Cen1School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, ChinaMany components of electric vehicles contain rolling bearings, and the operating condition of rolling bearings often affects the operating performance of electric vehicles. Monitoring the operating status of the bearings is one of the key technologies to ensure the safe operation of the bearings. We propose a channel attention-based convolutional neural network (CA-CNN) model for rolling bearing fault diagnosis. The model can directly use the raw vibration signal of the bearing as input to achieve bearing fault diagnosis under different operating loads and different noise environments. The experimental results show that, compared with other intelligent diagnosis methods, the proposed model CA-CNN achieves a high diagnostic accuracy under different load cases and still has advantages in different noisy environments. It is also beneficial to promote the intelligent fault diagnosis and maintenance of electric vehicles.https://www.mdpi.com/2032-6653/13/11/208motor bearingfault diagnosisconvolutional neural networkschannel attentiondeep learning |
| spellingShingle | Jianguo Yin Gang Cen Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN World Electric Vehicle Journal motor bearing fault diagnosis convolutional neural networks channel attention deep learning |
| title | Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN |
| title_full | Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN |
| title_fullStr | Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN |
| title_full_unstemmed | Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN |
| title_short | Intelligent Motor Bearing Fault Diagnosis Using Channel Attention-Based CNN |
| title_sort | intelligent motor bearing fault diagnosis using channel attention based cnn |
| topic | motor bearing fault diagnosis convolutional neural networks channel attention deep learning |
| url | https://www.mdpi.com/2032-6653/13/11/208 |
| work_keys_str_mv | AT jianguoyin intelligentmotorbearingfaultdiagnosisusingchannelattentionbasedcnn AT gangcen intelligentmotorbearingfaultdiagnosisusingchannelattentionbasedcnn |