The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest
Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/2623081 |
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| _version_ | 1850228941322715136 |
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| author | Xiwen Qin Qiaoling Li Xiaogang Dong Siqi Lv |
| author_facet | Xiwen Qin Qiaoling Li Xiaogang Dong Siqi Lv |
| author_sort | Xiwen Qin |
| collection | DOAJ |
| description | Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method. |
| format | Article |
| id | doaj-art-261810227d4e49848528ba1ccbb88136 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-261810227d4e49848528ba1ccbb881362025-08-20T02:04:22ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/26230812623081The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random ForestXiwen Qin0Qiaoling Li1Xiaogang Dong2Siqi Lv3School of Basic Sciences, Changchun University of Technology, Changchun 130012, ChinaSchool of Basic Sciences, Changchun University of Technology, Changchun 130012, ChinaSchool of Basic Sciences, Changchun University of Technology, Changchun 130012, ChinaSchool of Basic Sciences, Changchun University of Technology, Changchun 130012, ChinaAccurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance. A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected. Then their energy entropy is calculated as the feature. Finally, the classification is performed by RF. In addition, the wavelet method is also used in the proposed process, the same as EEMD. The results of the comparison show that the EEMD method is more accurate than the wavelet method.http://dx.doi.org/10.1155/2017/2623081 |
| spellingShingle | Xiwen Qin Qiaoling Li Xiaogang Dong Siqi Lv The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest Shock and Vibration |
| title | The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest |
| title_full | The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest |
| title_fullStr | The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest |
| title_full_unstemmed | The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest |
| title_short | The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest |
| title_sort | fault diagnosis of rolling bearing based on ensemble empirical mode decomposition and random forest |
| url | http://dx.doi.org/10.1155/2017/2623081 |
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