A Fault Feature Reduction Method Based on Rough Set Attribute Reduction and Principal Component Analysis
Huang, Qiang1; Wang, Jian1; Su, Haixia1; Yang, Lu1; Ding, Zhaoping2; Zhang, Guigang1
2016
会议名称2016 35th Chinese Control Conference (CCC)
会议录名称Control Conference (CCC), 2016 35th Chinese
会议日期2016年7月27-29日
会议地点四川成都
摘要Recently, precise diagnosis of faults is increasingly taken seriously, and the fault feature reduction is one of the key technologies to carry out accurate and reliable diagnosis. In this paper, a feature reduction method based on rough set attribute reduction and principal component analysis is proposed. Firstly the rough set attribute reduction is used to remove the irrelevant features, and then the principal component analysis is adopted to further reduce the features. Finally, the validity of the method is verified by the aero engine rotor fault data. Experimental results show that the proposed method can not only improve the accuracy of fault diagnosis, but also reduce the number of fault features and improve the diagnostic efficiency.
关键词Feature Reduction Rough Set Attribute Reduction Principal Component Analysis Aero Engine Rotor Fault
DOI10.1109/ChiCC.2016.7554399
收录类别EI
语种英语
引用统计
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13021
专题数字内容技术与服务研究中心_智能技术与系统工程
通讯作者Wang, Jian
作者单位1.Institute of Automation, Chinese Academy of Science
2.AVIX Jiangxi Hongdu Aviation Industry Group Company Ltd
推荐引用方式
GB/T 7714
Huang, Qiang,Wang, Jian,Su, Haixia,et al. A Fault Feature Reduction Method Based on Rough Set Attribute Reduction and Principal Component Analysis[C],2016.
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