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Received: 2007-11-06

Revision Accepted: 2008-03-10

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.8 P.1061~1069

http://doi.org/10.1631/jzus.A0720051


Batch process monitoring based on multilevel ICA-PCA


Author(s):  Zhi-qiang GE, Zhi-huan SONG

Affiliation(s):  State Key Lab of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zqge@iipc.zju.edu.cn, zhsong@iipc.zju.edu.cn

Key Words:  Multilevel, Independent component analysis (ICA), Principal component analysis (PCA), Batch process monitoring, Non-Gaussian


Zhi-qiang GE, Zhi-huan SONG. Batch process monitoring based on multilevel ICA-PCA[J]. Journal of Zhejiang University Science A, 2008, 9(8): 1061~1069.

@article{title="Batch process monitoring based on multilevel ICA-PCA",
author="Zhi-qiang GE, Zhi-huan SONG",
journal="Journal of Zhejiang University Science A",
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pages="1061~1069",
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doi="10.1631/jzus.A0720051"
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%T Batch process monitoring based on multilevel ICA-PCA
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%A Zhi-huan SONG
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 8
%P 1061~1069
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720051

TY - JOUR
T1 - Batch process monitoring based on multilevel ICA-PCA
A1 - Zhi-qiang GE
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J0 - Journal of Zhejiang University Science A
VL - 9
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SP - 1061
EP - 1069
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0720051


Abstract: 
In this paper, we describe a new batch process monitoring method based on multilevel independent component analysis and principal component analysis (MLICA-PCA). Unlike the conventional multi-way principal component analysis (MPCA) method, MLICA-PCA provides a separated interpretation for multilevel batch process data. Batch process data are partitioned into two levels: the within-batch level and the between-batch level. In each level, the Gaussian and non-Gaussian components of process information can be separately extracted. I2, T2 and SPE statistics are individually built and monitored. The new method facilitates fault diagnosis. Since the two variation levels are decomposed, the variables responsible for faults in each level can be identified and interpreted more easily. A case study of the Dupont benchmark process showed that the proposed method was more efficient and interpretable in fault detection and diagnosis, compared to the alternative batch process monitoring method.

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

Reference

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