Full Text:   <2219>

CLC number: TP273

On-line Access: 

Received: 2007-11-06

Revision Accepted: 2008-03-10

Crosschecked: 0000-00-00

Cited: 7

Clicked: 3494

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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


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",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Batch process monitoring based on multilevel ICA-PCA
%A Zhi-qiang GE
%A Zhi-huan SONG
%J Journal of Zhejiang University SCIENCE A
%V 9
%N 8
%P 1061~1069
%@ 1673-565X
%D 2008
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0720051

T1 - Batch process monitoring based on multilevel ICA-PCA
A1 - Zhi-qiang GE
A1 - Zhi-huan SONG
J0 - Journal of Zhejiang University Science A
VL - 9
IS - 8
SP - 1061
EP - 1069
%@ 1673-565X
Y1 - 2008
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0720051

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


[1] Camacho, J., Picó, J., 2006. Online monitoring of batch processes using multi-phase principal component analysis. J. Process Control, 16(10):1021-1035.

[2] Chen, Q., Kruger, U., Leung, A.T.Y., 2004. Regularised kernel density estimation for clustered process data. Control Eng. Pract., 12(3):267-274.

[3] de Noord, O.E., Theobald, E.H., 2005. Multilevel component analysis and multilevel PLS of chemical process data. J. Chemometr., 19(5-7):301-307.

[4] Doan, X.T., Srinivasan, R., 2008. Online monitoring of multi-phase batch processes using phase-based multivariate statistical process control. Comput. Chem. Eng., 32(1-2):230-243.

[5] Ge, Z.Q., Song, Z.H., 2007. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors. Ind. Eng. Chem. Res., 46(7):2054-2063.

[6] Hyvarinen, A., Oja, E., 2000. Independent component analysis: algorithms and applications. Neural Network, 13(4-5):411-430.

[7] Jansen, J.J., Hoefsloot, H.C.J., van der Greef, J., Timmerman, M.E., Smilde, A.K., 2005. Multilevel component analysis of time-resolved metabolic fingerprinting data. Anal. Chim. Acta, 530(2):173-183.

[8] Kano, M., Tanaka, S., Hasebe, S., Hashimoto, I., Ohno, H., 2003. Monitoring independent components for fault detection. AIChE J., 49(4):969-976.

[9] Kiers, H.A.L., Ten Berge, J.M.F., 1994. Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure. Br. J. Math. Psychol., 47:109-126.

[10] Lee, J.M., Yoo, C.K., Lee, I.B., 2004. Statistical process monitoring with independent component analysis. J. Process Control, 14(5):467-485.

[11] Lee, J.M., Qin, S.J., Lee, I.M., 2006. Fault detection and diagnosis based on modified independent component analysis. AIChE J., 52(10):3501-3514.

[12] Nomikos, P., MacGregor, J.F., 1994. Monitoring batch processes using multiway principal component analysis. AIChE J., 40(8):1361-1375.

[13] Nomikos, P., MacGregor, J.F., 1995a. Multivariate SPC charts for monitoring batch process. Technometrics, 37(1):41-59.

[14] Nomikos, P., MacGregor, J.F., 1995b. Multi-way partial least square in monitoring batch processes. Chemometr. Intell. Lab. Syst., 30(1):97-108.

[15] Simoglou, A., Georgieva, P., Martin, E.B., Morris, A.J., de Azevedo, S.F., 2005. On-line monitoring of a sugar crystallization process. Comput. Chem. Eng., 29(6):1411-1422.

[16] Timmerman, M.E., Kiers, H.A.L., 2003. Four simultaneous component models for the analysis of multivariate time series for more than one subject to model intraindividual and interindividual differences. Psychometrika, 68(1):105-121.

[17] Yoo, C.K., Lee, J.M., Vanrolleghem, P.A., Lee, I.B., 2004. On-line monitoring of batch processes using multiway independent component analysis. Chemometr. Intell. Lab. Syst., 71(2):151-163.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE