Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network
A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Networ...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2017/9602650 |
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| author | Kai Chen Xin-Cong Zhou Jun-Qiang Fang Peng-fei Zheng Jun Wang |
| author_facet | Kai Chen Xin-Cong Zhou Jun-Qiang Fang Peng-fei Zheng Jun Wang |
| author_sort | Kai Chen |
| collection | DOAJ |
| description | A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application. |
| format | Article |
| id | doaj-art-633b6804f13b45c1a7c0aed90fedfbd5 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-633b6804f13b45c1a7c0aed90fedfbd52025-02-03T00:59:14ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342017-01-01201710.1155/2017/96026509602650Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs NetworkKai Chen0Xin-Cong Zhou1Jun-Qiang Fang2Peng-fei Zheng3Jun Wang4Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan 430063, ChinaA gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.http://dx.doi.org/10.1155/2017/9602650 |
| spellingShingle | Kai Chen Xin-Cong Zhou Jun-Qiang Fang Peng-fei Zheng Jun Wang Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network International Journal of Rotating Machinery |
| title | Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network |
| title_full | Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network |
| title_fullStr | Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network |
| title_full_unstemmed | Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network |
| title_short | Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network |
| title_sort | fault feature extraction and diagnosis of gearbox based on eemd and deep briefs network |
| url | http://dx.doi.org/10.1155/2017/9602650 |
| work_keys_str_mv | AT kaichen faultfeatureextractionanddiagnosisofgearboxbasedoneemdanddeepbriefsnetwork AT xincongzhou faultfeatureextractionanddiagnosisofgearboxbasedoneemdanddeepbriefsnetwork AT junqiangfang faultfeatureextractionanddiagnosisofgearboxbasedoneemdanddeepbriefsnetwork AT pengfeizheng faultfeatureextractionanddiagnosisofgearboxbasedoneemdanddeepbriefsnetwork AT junwang faultfeatureextractionanddiagnosisofgearboxbasedoneemdanddeepbriefsnetwork |