文献类型：期刊文献

英文题名：A novel method for detecting processes with multi-state modes

作者：Wang, Xiaoyang[1];Wang, Xin[2];Wang, Zhenlei[1];Qian, Feng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2013

卷号：21

期号：12

起止页码：1788

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20134817027987);WOS:【SCI-EXPANDED(收录号:WOS:000329017200014)】；

基金：The authors gratefully acknowledge the financial support of the National Natural Science Foundation of China (Key Program: 61134007), National High-Tech Research and Development Program of China (2012AA040307), Shanghai Leading Academic Discipline Project (B504) and State Key Laboratory of Synthetical Automation for Process Industries.

语种：英文

外文关键词：FCM; CloneDE-HS; MVU; SVDD; Multiple-model detecting method

摘要：Aiming at the muiltimode non-Gaussian process with within-mode nonlinearity, a fuzzy clustering multiple-model based inferential detecting method was proposed in this article. A clone-differential evolution-harmony search algorithm (CloneDE-HS) is used to search the best clustering centers of the process data. Then the operating data were classified as different modes. After that, maximum variance unfolding (MVU) were used to reduce the dimensions of each submodel variables. Furthermore the monitoring indices were constructed to detect the process fault. The model based support vector data description (SVDD) was built to detect the process. Finally, the proposed method was applied to detect an ethylene cracking furnace to demonstrate its efficiency. (C) 2013 Elsevier Ltd. All rights reserved.