PLS-based Similarity Analysis for Mode Identification in Multimode Manufacturing Processes

被引：8

作者：

Zheng, Ying ^{[1
]}

Qin, S. Joe ^{[2
]}

Wang, Fu-li ^{[3
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China

[2] Chinese Univ Hong Kong, 2001 Longxiang Blvd, Shenzhen, Guangdong, Peoples R China

[3] Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Peoples R China

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 08期

关键词：

process monitoring; multiple operation mode; mode identification; partial least square(PLS); external analysis; STATISTICAL PROCESS-CONTROL; EASTMAN CHALLENGE PROCESS; EXTERNAL ANALYSIS; BATCH;

D O I：

10.1016/j.ifacol.2015.09.063

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Many industrial manufacturing processes have multiple operation modes because of different strategy-and varying feedstock. The traditional statistical process monitoring tools such as PCA and PLS cannot be applied since they assume that the process must have single mode operation region only. In this paper, all the factors that will affect the change of the mode are considered, a similarity factor including the similarity factor of PLS models and the mean shift of the external variables is introduced to measure the similarity of two sets of data. On basis of this similarity factor, a moving window is used and a mode identification approach for multimode process monitoring is proposed. The proposed approach is demonstrated on the benchmark Tennessee Eastman process. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

引用

页码：777 / 782

页数：6

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