FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN
In order to improve the rolling bearing fault diagnosis accuracy,this paper presents a fault diagnosis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Convolution Neural Networks（ CNN）. At first,using the EEMD decompose the signal. After that,choose appropriate IMFs according to the...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Mechanical Strength
2020-01-01
|
| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.05.003 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841535766074753024 |
|---|---|
| author | LI SiQi JIANG ZhiJian |
| author_facet | LI SiQi JIANG ZhiJian |
| author_sort | LI SiQi |
| collection | DOAJ |
| description | In order to improve the rolling bearing fault diagnosis accuracy,this paper presents a fault diagnosis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Convolution Neural Networks（ CNN）. At first,using the EEMD decompose the signal. After that,choose appropriate IMFs according to the correlation coefficent and kurtosis calculating results to reconstruct the signal. After calculating a series of indexes of reconstructed signals,using CNN and various methods to diagnose faults. The results shows that the method used in this paper can effectively carry out fault diagnosis. The accuracy can reach 96. 7%. It has certain application significance to fault diagnosis. |
| format | Article |
| id | doaj-art-3ddc22938f524054baca5100bed13129 |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-3ddc22938f524054baca5100bed131292025-01-15T02:27:11ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692020-01-01421033103830608944FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNNLI SiQiJIANG ZhiJianIn order to improve the rolling bearing fault diagnosis accuracy,this paper presents a fault diagnosis method based on Ensemble Empirical Mode Decomposition（ EEMD） and Convolution Neural Networks（ CNN）. At first,using the EEMD decompose the signal. After that,choose appropriate IMFs according to the correlation coefficent and kurtosis calculating results to reconstruct the signal. After calculating a series of indexes of reconstructed signals,using CNN and various methods to diagnose faults. The results shows that the method used in this paper can effectively carry out fault diagnosis. The accuracy can reach 96. 7%. It has certain application significance to fault diagnosis.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.05.003Ensemble empirical mode decompositionConvolution neural networkFault diagnosisMachine learning |
| spellingShingle | LI SiQi JIANG ZhiJian FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN Jixie qiangdu Ensemble empirical mode decomposition Convolution neural network Fault diagnosis Machine learning |
| title | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN |
| title_full | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN |
| title_fullStr | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN |
| title_full_unstemmed | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN |
| title_short | FAULT DIAGNOSIS METHOD OF ROLLING BEARING BASED ON EEMD-CNN |
| title_sort | fault diagnosis method of rolling bearing based on eemd cnn |
| topic | Ensemble empirical mode decomposition Convolution neural network Fault diagnosis Machine learning |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2020.05.003 |
| work_keys_str_mv | AT lisiqi faultdiagnosismethodofrollingbearingbasedoneemdcnn AT jiangzhijian faultdiagnosismethodofrollingbearingbasedoneemdcnn |