Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework
Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. A...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/6/1555 |
| _version_ | 1797541744118595584 |
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| author | Hui Li Fan Li Rong Jia Fang Zhai Liang Bai Xingqi Luo |
| author_facet | Hui Li Fan Li Rong Jia Fang Zhai Liang Bai Xingqi Luo |
| author_sort | Hui Li |
| collection | DOAJ |
| description | Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University. |
| first_indexed | 2024-03-10T13:20:06Z |
| format | Article |
| id | doaj.art-33f6017e1f164790bd6354e55e4ba9e9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:20:06Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-33f6017e1f164790bd6354e55e4ba9e92023-11-21T10:05:27ZengMDPI AGEnergies1996-10732021-03-01146155510.3390/en14061555Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost FrameworkHui Li0Fan Li1Rong Jia2Fang Zhai3Liang Bai4Xingqi Luo5School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Electrical Engineering, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Humanities and Foreign Languages, Xi’an University of Technology, Xi’an 710054, ChinaInstitute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710054, ChinaInstitute of Water Resources and Hydro-Electric Engineering, Xi’an University of Technology, Xi’an 710054, ChinaSymplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University.https://www.mdpi.com/1996-1073/14/6/1555rolling bearingssymplectic geometric mode decompositioncosine similaritysymplectic geometric entropyAdaBoost |
| spellingShingle | Hui Li Fan Li Rong Jia Fang Zhai Liang Bai Xingqi Luo Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework Energies rolling bearings symplectic geometric mode decomposition cosine similarity symplectic geometric entropy AdaBoost |
| title | Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework |
| title_full | Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework |
| title_fullStr | Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework |
| title_full_unstemmed | Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework |
| title_short | Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework |
| title_sort | research on the fault feature extraction of rolling bearings based on sgmd cs and the adaboost framework |
| topic | rolling bearings symplectic geometric mode decomposition cosine similarity symplectic geometric entropy AdaBoost |
| url | https://www.mdpi.com/1996-1073/14/6/1555 |
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