JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2013 Vol.14 No.7 P.520-534

Statistical process monitoring based on improved principal component analysis and its application to chemical processes*

Author(s): Chu-dong Tong, Xue-feng Yan, Yu-xin Ma
Affiliation(s): . Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
Corresponding email(s): xfyan@ecust.edu.cn
Key Words: Fault detection, Principal component analysis (PCA), Correlative principal components (CPCs), Tennessee Eastman process

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Chu-dong Tong, Xue-feng Yan, Yu-xin Ma. Statistical process monitoring based on improved principal component analysis and its application to chemical processes[J]. Journal of Zhejiang University Science A, 2013, 14(7): 520-534.

@article{title="Statistical process monitoring based on improved principal component analysis and its application to chemical processes",
author="Chu-dong Tong, Xue-feng Yan, Yu-xin Ma",
journal="Journal of Zhejiang University Science A",
volume="14",
number="7",
pages="520-534",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1300003"
}

%0 Journal Article
%T Statistical process monitoring based on improved principal component analysis and its application to chemical processes
%A Chu-dong Tong
%A Xue-feng Yan
%A Yu-xin Ma
%J Journal of Zhejiang University SCIENCE A
%V 14
%N 7
%P 520-534
%@ 1673-565X
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1300003

TY - JOUR
T1 - Statistical process monitoring based on improved principal component analysis and its application to chemical processes
A1 - Chu-dong Tong
A1 - Xue-feng Yan
A1 - Yu-xin Ma
J0 - Journal of Zhejiang University Science A
VL - 14
IS - 7
SP - 520
EP - 534
%@ 1673-565X
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1300003

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling’s T ² statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

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