文献类型：期刊文献

英文题名：An adaptive multimode process monitoring strategy based on mode clustering and mode unfolding

作者：Tong, Chudong[1,2];Palazoglu, Ahmet[2];Yan, Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Univ Calif Davis, Dept Chem Engn & Mat Sci, Davis, CA 95616 USA

年份：2013

卷号：23

期号：10

起止页码：1497

外文期刊名：JOURNAL OF PROCESS CONTROL

收录：;EI(收录号：20134616976364);WOS:【SCI-EXPANDED(收录号:WOS:000328721400015)】；

基金：The support from 973 project of China (2013CB733600), National Natural Science Foundation of China (21176073), Program for New Century Excellent Talents in University (NCET-09-0346) and the China Scholarship Council is gratefully acknowledged.

语种：英文

外文关键词：Fault detection; Clustering; Principal component analysis; Multimode processes

摘要：A comprehensive monitoring framework is proposed for multimode processes in which mode clustering and mode unfolding are integrated within an adaptive strategy. To start, an aggregated k-means algorithm produces an optimal ensemble clustering solution for a multimode process dataset. Next, a mode unfolding (MU) scheme enables the development of a single principal component analysis (PCA) model for processes operating under multiple desired steady-states (modes). Finally, adaptive strategies for online mode identification and model updating are presented to address the challenges in fault detection in the presence of multiple operating modes. The validity and usefulness of the adaptive MU-PCA based monitoring framework is demonstrated through a study of the Tennessee Eastman benchmark process. (C) 2013 Elsevier Ltd. All rights reserved.