An improved KPCA algorithm of chemical process fault diagnosis based on RVM

	An improved KPCA algorithm of chemical process fault diagnosis based on RVM
	Zhao, Xiaoqiang; Xue, Yongfei; Yang, Wu
	2013-10-18
会议日期	July 26, 2013 - July 28, 2013
会议地点	Xi'an, China
关键词	Failure analysis Principal component analysis Process control Support vector machines Vector spaces Vectors Combined algorithms Fault identifications Kernel principal component analyses (KPCA) KPCA-RVM KPCA-SVM Relevance Vector Machine TE process Tennessee Eastman
页码	6083-6087
英文摘要	KPCA-SVM algorithm is a combination of kernel principal component analysis (KPCA) and support vector machine (SVM). It could increase the diagnosis time and decrease the diagnosis efficiency, because more relevant vectors are needed when it is used to monitor the on-line complex chemical process. According to this problem, another combined algorithm which is composed of kernel principal component analysis and relevance vector machine (RVM) is proposed in this paper. Firstly, KPCA-RVM algorithm uses KPCA to structure T2 statistics and SPE statistics in the feature space to detect fault, and then it takes the non-linear principal component score vector of samples as the input of relevance vector machine to identify the fault modes. KPCA-RVM algorithm is applied to Tennessee Eastman (TE) chemical process and many kinds of fault mode simulation results show that this algorithm not only can obtain higher fault diagnosis accuracy than KPCA-SVM, but also can raise the speed of fault diagnosis obviously owing to the less necessary relevant vectors. © 2013 TCCT, CAA.
会议录	Chinese Control Conference, CCC
会议录出版者	IEEE Computer Society
语种	中文
ISSN号	19341768
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117519]
专题	电气工程与信息工程学院
作者单位	College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
推荐引用方式 GB/T 7714	Zhao, Xiaoqiang,Xue, Yongfei,Yang, Wu. An improved KPCA algorithm of chemical process fault diagnosis based on RVM[C]. 见:. Xi'an, China. July 26, 2013 - July 28, 2013.