Performance monitoring of processes with multiple operating modes through multiple PLS models

被引:199
作者
Zhao, Shi Jian
Zhang, Jie [1 ]
Xu, Yong Mao
机构
[1] Univ Newcastle Upon Tyne, Sch Chem Engn & Adv Mat, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[2] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China
基金
英国工程与自然科学研究理事会; 国家高技术研究发展计划(863计划);
关键词
projection to latent structures; principal angles; multiple operating modes; multivariate statistical process monitoring;
D O I
10.1016/j.jprocont.2005.12.002
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 processes possess multiple operating modes in virtue of different manufacturing strategies or varying feedstock. Direct application of many of the current multivariate statistical process monitoring (MSPM) techniques such as PCA (principal component analysis) and PLS (projection to latent structures) to such a process tends to produce inferior performance. This can most be attributed to the adopted assumption by most MSPM methodologies of only one nominal operating region for the underlying process. It is therefore reasonable to develop separate models for different operating modes. In this paper, based on metrics in the form of principal angles to measure the similarities of any two models, a multiple PLS model based process monitoring scheme is proposed. Popular multivariate statistics such as SPE (squared prediction error) and T-2 can be incorporated in this framework straightforwardly. The proposed technique is assessed through application to the monitoring of an industrial pyrolysis furnace. (C) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:763 / 772
页数:10
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