Improved principal component monitoring of large-scale processes

被引：169

作者：

Kruger, U

Zhou, YQ

Irwin, GW

机构：

[1] Queens Univ Belfast, Sch Elect & Elect Engn, Intelligent Syst & Control Res Grp, Belfast BT9 5AH, Antrim, North Ireland

[2] Shandong Univ, Sch Mech Engn, Jinan 250061, Peoples R China

来源：

JOURNAL OF PROCESS CONTROL | 2004年 / 14卷 / 08期

关键词：

multivariate statistical process control; ARMA filters; auto-correlated process variables;

D O I：

10.1016/j.jprocont.2004.02.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 [计算机科学与技术];

摘要：

In this work, the integration of ARMA filters into the multivariate statistical process control (MSPC) framework is presented to improve the monitoring of large-scale industrial processes. As demonstrated in the paper, such filters can remove auto-correlation from the monitored variables to avoid the production of false alarms. This is exemplified by application studies to a synthetic example from the literature and to the Tennessee Eastman benchmark process. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：879 / 888

页数：10

共 21 条

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