Fault Diagnosis Based on GMM of KCVA for Chemical Process

	Fault Diagnosis Based on GMM of KCVA for Chemical Process
	Zhao Xiaoqiang 1,2; Zhang Xiaoxiao 2
	2015
关键词	fault diagnosis Gaussian mixture model(GMM) kernel canonical variate analysis(KCVA) TE process
页码	2690-2695
英文摘要	Usually, there are multiple operate modes in chemical process because of producing kinds of product. traditional fault diagnosis methods for single operate mode arc no longer applicable when they used to diagnose process of multiple operate models. therefore, This paper proposes a algorithm of kernel canonical variate analysis based on Gaussian Mixture Model, first of all, history data of chemical process is decomposed to multiple Gaussian components by using Gaussian Mixture Model (GMM), then using kernel canonical variate analysis(KCVA) algorithm to model for each Gaussian component, calculating the corresponding statistics for process monitoring. In the TE process simulation, comparison with KCVA algorithm, fault diagnosis result illustrate the effectiveness of the proposed algorithm in this paper.
会议录	2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC)
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	中文
WOS研究方向	Automation & Control Systems ; Engineering
WOS记录号	WOS:000375232904008
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36532]
专题	电气工程与信息工程学院
通讯作者	Zhao Xiaoqiang
作者单位	1.Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China 2.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Zhao Xiaoqiang,Zhang Xiaoxiao. Fault Diagnosis Based on GMM of KCVA for Chemical Process[C]. 见:.