文献类型：会议论文

英文题名：Fault detection for a class of industrial processes based on recursive multiple models

作者：Gao Yong[1];Wang Xin[2];Wang Zhenlei[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200030, Peoples R China

会议论文集：11th World Congress on Intelligent Control and Automation (WCICA)

会议日期：JUN 27-30, 2014

会议地点：Shenyang, PEOPLES R CHINA

语种：英文

外文关键词：Multiple models; Time-varying; RKPCA; SVDD

摘要：Traditional multiple model process monitoring methods usually yield satisfactory results for multi-mode processes under the assumption that the processes are time invariant. However, for some petrochemical processes, such as ethylene cracking furnace, the process is time varying as the coking in the furnace tubes. To solve this problem, this study proposes a multiple model recursive monitoring method. A computational intelligence-based cluster algorithm is employed to separate different operating modes. Then, recursive kernel principal component analysis is used to reduce the dimension of the time-varying process data and extract the nonlinear principal components recursively. Furthermore, support vector data description is utilized to build models because the process data are non-Gaussian, Finally, the corresponding statistics are constructed to detect the process fault. The performance of this method is evaluated through a case study of ethylene cracking in a petrochemical plant. (C) 2015 Elsevier B.V. All rights reserved.