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CLC number: TQ086.3; TP277

On-line Access: 2013-07-01

Received: 2013-01-02

Revision Accepted: 2013-04-12

Crosschecked: 2013-06-08

Cited: 5

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Citations:  Bibtex RefMan EndNote GB/T7714

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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.

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T1 - Statistical process monitoring based on improved principal component analysis and its application to chemical processes
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A1 - Yu-xin Ma
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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