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An intelligent fault diagnosis of rolling bearing based on EMD and correlation analysis

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成果类型:
会议论文
作者:
Li Jianbao;Peng Tao
通讯作者:
Li, JB
作者机构:
[Li Jianbao; Peng Tao] School of Electrical and Information Engineering, Hunan University Of Technology, Zhuzhou Hunan 412008, China
通讯机构:
[Li Jianbao] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412008, Hunan, Peoples R China.
语种:
中文
关键词:
Empirical Mode Decomposition;Correlation Analysis;Support Vector Machines;Fault Diagnosis;Rolling Bearing
期刊:
Proceedings of the 29th Chinese Control Conference, CCC'10
ISSN:
2161-2927
年:
2010
页码:
3931-3936
会议名称:
29th Chinese Control Conference
会议论文集名称:
Chinese Control Conference
会议时间:
JUL 29-31, 2010
会议地点:
Beijing, PEOPLES R CHINA
会议主办单位:
[Li Jianbao;Peng Tao] Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412008, Hunan, Peoples R China.
会议赞助商:
Beijing Inst Technol, Control Syst Soc, Soc ICE Japan, ICROS Korea, CAA, Tech Comm Control Theory, Beihand Univ Press, IEEE Control Syst Soc, IEEE CSS Singapore Chapter, IEEE CSS Hong Kong Chapter, IEEE CSS Beijing Chapter, CAS, Acad Math & Syst Sci, Tsinghua Univ, Peking Univ, Beijing Univ Aeronaut & Astronaut
主编:
Chen, J
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-7-89463-104-6
文献类别:
WOS:Proceedings Paper;EI:Conference article (CA)
所属学科:
WOS学科类别:Automation & Control Systems;Engineering, Electrical & Electronic
入藏号:
WOS:000397331304010;EI:20105113503687
机构署名:
本校为第一且通讯机构
院系归属:
电气与信息工程学院
摘要:
A feature extraction method using joint empirical mode decomposition (EMD) and correlation analysis is proposed. The vibration signal of a rolling bearing is decomposed into a number of IMF (Intrinsic Mode Function) components by using EMD method, after adopting large numbers of correlation analysis of the IMF components and vibration signal is decomposed, we found that the correlation coefficients between IMF and vibration signal is decomposed have great differences under different state, and can be regarded as the feature vectors of the bearing. Finally, the running state of bearing is recognized and classified by using support vector machine (SVM) classifier. The simulation result shows the effectiveness and feasibility of the proposed approach for recognizing the state of rolling bearing.
参考文献:
Cheng JS, 2006, MECH SYST SIGNAL PR, V20, P350, DOI 10.1016/j.ymssp.2004.11.002
Huang NE, 1998, P ROY SOC A-MATH PHY, V454, P903
Vapnik VN, 1999, IEEE T NEURAL NETWOR, V10, P988, DOI 10.1109/72.788640
Widodo A, 2007, MECH SYST SIGNAL PR, V21, P2560, DOI 10.1016/j.ymssp.2006.12.007
Yang JY, 2007, MECH SYST SIGNAL PR, V21, P2012, DOI 10.1016/j.ymssp.2006.10.005

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