ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN
Aiming at problems that traditional fault diagnosis methods need to extract features manually and the recognition rate is low, a VMD-CWT-CNN model based on variational modal decomposition (VMD) and continuous wavelet transform (CWT) combined with convolutional neural network (CNN) is proposed for ro...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-12-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.002 |
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| _version_ | 1841534199741284352 |
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| author | CHEN DaiJun CHEN LiLi DONG ShaoJiang |
| author_facet | CHEN DaiJun CHEN LiLi DONG ShaoJiang |
| author_sort | CHEN DaiJun |
| collection | DOAJ |
| description | Aiming at problems that traditional fault diagnosis methods need to extract features manually and the recognition rate is low, a VMD-CWT-CNN model based on variational modal decomposition (VMD) and continuous wavelet transform (CWT) combined with convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, the bearing vibration signal is decomposed into multiple modal components with different center frequencies by VMD. Secondly, the modal components are calculated by CWT and transformed into two-dimensional time-frequency diagram. Finally, the time-frequency diagram is input into the EfficientNet convolution neural network after structure cutting, the features are automatically extracted, and the fault diagnosis of rolling bearing is completed. Using the method proposed, the average accuracy of multiple experiments on 10 types of bearing fault data from Case Western Reserve University is 99. 86%, which can effectively complete the feature extraction of rolling bearing signal and the accurate diagnosis of damage degree. |
| format | Article |
| id | doaj-art-be65a0167c364c81b43b81a60dcfe833 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-be65a0167c364c81b43b81a60dcfe8332025-01-15T02:45:04ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-12-01451280128555273447ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNNCHEN DaiJunCHEN LiLiDONG ShaoJiangAiming at problems that traditional fault diagnosis methods need to extract features manually and the recognition rate is low, a VMD-CWT-CNN model based on variational modal decomposition (VMD) and continuous wavelet transform (CWT) combined with convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, the bearing vibration signal is decomposed into multiple modal components with different center frequencies by VMD. Secondly, the modal components are calculated by CWT and transformed into two-dimensional time-frequency diagram. Finally, the time-frequency diagram is input into the EfficientNet convolution neural network after structure cutting, the features are automatically extracted, and the fault diagnosis of rolling bearing is completed. Using the method proposed, the average accuracy of multiple experiments on 10 types of bearing fault data from Case Western Reserve University is 99. 86%, which can effectively complete the feature extraction of rolling bearing signal and the accurate diagnosis of damage degree.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.002VMDCWTTime-frequency diagramCNNFault diagnosis |
| spellingShingle | CHEN DaiJun CHEN LiLi DONG ShaoJiang ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN Jixie qiangdu VMD CWT Time-frequency diagram CNN Fault diagnosis |
| title | ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN |
| title_full | ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN |
| title_fullStr | ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN |
| title_full_unstemmed | ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN |
| title_short | ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN |
| title_sort | rolling bearing fault diagnosis based on vmd cwt cnn |
| topic | VMD CWT Time-frequency diagram CNN Fault diagnosis |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.06.002 |
| work_keys_str_mv | AT chendaijun rollingbearingfaultdiagnosisbasedonvmdcwtcnn AT chenlili rollingbearingfaultdiagnosisbasedonvmdcwtcnn AT dongshaojiang rollingbearingfaultdiagnosisbasedonvmdcwtcnn |