A Fault Feature Reduction Method Based on Rough Set Attribute Reduction and Principal Component Analysis

doi:10.1109/ChiCC.2016.7554399

CORC > 自动化研究所 > 中国科学院自动化研究所 > 数字内容技术与服务研究中心 > 智能技术与系统工程

	A Fault Feature Reduction Method Based on Rough Set Attribute Reduction and Principal Component Analysis
	Huang, Qiang1 ; Wang, Jian 1; Su, Haixia1 ; Yang, Lu 1; Ding, Zhaoping 2; Zhang, Guigang1
	2016
会议日期	2016年7月27-29日
会议地点	四川成都
关键词	Feature Reduction Rough Set Attribute Reduction Principal Component Analysis Aero Engine Rotor Fault
DOI	10.1109/ChiCC.2016.7554399
英文摘要	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.
会议录	Control Conference (CCC), 2016 35th Chinese
语种	英语
内容类型	会议论文
源URL	[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]. 见:. 四川成都. 2016年7月27-29日.