Fault diagnosis based on GMM of KCVA for chemical process

doi:10.1109/CCDC.2015.7162379

	Fault diagnosis based on GMM of KCVA for chemical process
	Zhao, Xiaoqiang 1,2; Zhang, Xiaoxiao 1
	2015-07-17
会议日期	May 23, 2015 - May 25, 2015
会议地点	Qingdao, China
DOI	10.1109/CCDC.2015.7162379
页码	2964-2969
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 traditional fault diagnosis methods for single operate mode are no longer applicable when they used to diagnose process of multiple operate models, therefore, This paper proposes a algorithm of kernel canonical variate analysis based on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. © 2015 IEEE.
会议录	Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
会议录出版者	Institute of Electrical and Electronics Engineers Inc.
语种	中文
内容类型	会议论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/117698]
专题	电气工程与信息工程学院
作者单位	1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China; 2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou, 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:. Qingdao, China. May 23, 2015 - May 25, 2015.