Online monitoring of nonlinear multiple mode processes based on adaptive local model approach

被引：176

作者：

Ge, Zhiqiang ^{[1
]}

Song, Zhihuan ^{[1
]}

机构：

[1] Zhejiang Univ, Inst Ind Proc Control, State Key Lab Ind Control Technol, Hangzhou 310027, Zhejiang, Peoples R China

来源：

CONTROL ENGINEERING PRACTICE | 2008年 / 16卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Online process monitoring; Multiple mode; Least square support vector regression; Local model approach; Two-step information extraction strategy;

D O I：

10.1016/j.conengprac.2008.04.004

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new adaptive local model based monitoring approach is proposed for online monitoring of nonlinear multiple mode processes with non-Gaussian information. To solve the multiple mode problem, just-intime-learning (JITL) strategy is introduced. The local least squares support vector regression (LSSVR) model is built on the relevant dataset for prediction. To satisfy the online modeling demand, the realtime problem is considered. Then a two-step independent component analysis-principal component analysis (ICA-PCA) information extraction strategy is introduced to analyze residuals between the real output and the predicted one. Two case studies show that the new proposed method gives better performance compared to conventional methods. (c) 2008 Elsevier Ltd. All rights reserved.

引用

页码：1427 / 1437

页数：11

共 43 条

[31] Recursive PCA for adaptive process monitoring [J].