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CLC number: TP27

On-line Access: 2010-12-09

Received: 2010-04-13

Revision Accepted: 2010-06-10

Crosschecked: 2010-06-29

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.12 P.948-955


Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes

Author(s):  Ying-wei Zhang, Yong-dong Teng

Affiliation(s):  MOE Key Lab of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China

Corresponding email(s):   zhangyingwei@mail.neu.edu.cn

Key Words:  Recursive multiblock kernel principal component analysis (RMBPCA), Dynamic process, Nonlinear process

Ying-wei Zhang, Yong-dong Teng. Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes[J]. Journal of Zhejiang University Science C, 2010, 11(12): 948-955.

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author="Ying-wei Zhang, Yong-dong Teng",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

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%T Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
%A Ying-wei Zhang
%A Yong-dong Teng
%J Journal of Zhejiang University SCIENCE C
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%N 12
%P 948-955
%@ 1869-1951
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000148

T1 - Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
A1 - Ying-wei Zhang
A1 - Yong-dong Teng
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 12
SP - 948
EP - 955
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1000148

Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.

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


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