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Fault diagnosis of rotating machinery based on FDR feature selection algorithm and SVM

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成果类型:
期刊论文、会议论文
作者:
Li, Sheng;Zhang, Chunliang*;Yue, Xia
通讯作者:
Zhang, Chunliang
作者机构:
[Li, Sheng] Univ S China, Sch Mech Engn, Hengyang 421001, Peoples R China.
[Zhang, Chunliang] Guangzhou Univ, Sch Mechan & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.
[Yue, Xia] S China Univ Technol, Sch Mechan & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China.
[Zhang, Chunliang] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.
通讯机构:
[Zhang, Chunliang] G
Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.
语种:
英文
关键词:
Fault Diagnosis;Feature Selection;Fisher’s Discriminant Ratio (FDR);Support Vector Machine (SVM)
期刊:
Advanced Materials Research
ISSN:
1022-6680
年:
2010
卷:
139-141
期:
280
页码:
2506-2512
会议名称:
International Conference on Manufacturing Engineering and Automation
会议论文集名称:
Advanced Materials Research
会议时间:
DEC 07-09, 2010
会议地点:
Guangzhou, PEOPLES R CHINA
会议主办单位:
[Li, Sheng] Univ S China, Sch Mech Engn, Hengyang 421001, Peoples R China.^[Zhang, Chunliang] Guangzhou Univ, Sch Mechan & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China.^[Yue, Xia] S China Univ Technol, Sch Mechan & Automot Engn, Guangzhou 510641, Guangdong, Peoples R China.
会议赞助商:
Guangzhou Univ, Univ New S Wales, Huazhong Univ Sci & Technol, Xian Jiaotong Univ
主编:
Zhang, LC Zhang, CL Shi, TL
出版地:
KREUZSTRASSE 10, 8635 DURNTEN-ZURICH, SWITZERLAND
出版者:
TRANS TECH PUBLICATIONS LTD
ISBN:
978-0-87849-226-8
基金类别:
National Defense Basic Scientific Research Project of China [B0120060585]; National Hi-tech Research and Development Program of ChinaNational High Technology Research and Development Program of China [2008AA04Z407]
机构署名:
本校为第一机构
院系归属:
机械工程学院
摘要:
To effectively avoid the loss of useful information, in this paper, feature information has been extracted from the fault signal of rotating machinery in different aspects such as amplitude-domain, time-domain and time-frequency domain. Then, for the multi-dimensional feature extraction was prone to the problem of “dimension disaster”, the principles of FDR was introduced in data mining to determine the classification ability of each individual feature, and the cross correlation coefficient was adopted to solve the problem that dealing with i...

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