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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

Cited: 1

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

http://doi.org/10.1631/jzus.C1000148


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",
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pages="948-955",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1000148"
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%T Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
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%A Yong-dong Teng
%J Journal of Zhejiang University SCIENCE C
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%P 948-955
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%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1000148

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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
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Y1 - 2010
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1000148


Abstract: 
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

Reference

[1]Cheng, C.Y., Hsu, C.C., Chen, M.C., 2010. Adaptive kernel principal component analysis (KPCA) for monitoring small disturbances of nonlinear processes. Ind. Eng. Chem. Res., 49(5):2254-2262.

[2]Chiang, L.H., Russell, F.L., Braatz, R.D., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London.

[3]Elshenawy, L.M., Yin, S., Naik, A.S., Ding, S.X., 2010. Efficient recursive principal component analysis algorithms for process monitoring. Ind. Eng. Chem. Res., 49(1):252-259.

[4]Gallagher, V.B., Wise, R.M., Butler, S.W., White, D.D., Barna, G.G., 1997. Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness Through Model Updating. Proc. ADCHEM, p.78-83.

[5]Ge, Z., Yang, C., Song, Z., 2009. Improved kernel PCA-based monitoring approach for nonlinear processes. Chem. Eng. Sci., 64(9):2245-2255.

[6]Jeng, J.C., Li, C.C., Huang, H.P., 2007. Fault detection and isolation for dynamic processes using recursive principal component analysis (PCA) based on filtering of signals. Asia-Pacific J. Chem. Eng., 2(6):501-509.

[7]Jia, F., Martin, E.B., Morris, A.J., 2000. Nonlinear principal components analysis with application to process fault detection. Int. J. Syst. Sci., 31(11):1473-1487.

[8]Kruger, U., Zhang, J., Xie, L., 2007. Principal Manifolds for Data Visualization and Dimension Reduction. In: Gorban, A.N., Kégl, B., Wunsch, D.C., et al. (Eds.), Lecture Notes in Computational Science and Engineering, Vol. 58. Springer.

[9]Liu, X., Kruger, U., Littler, T., Xie, L., Wang, S.Q., 2009. Moving window kernel PCA for adaptive monitoring of nonlinear processes. Chemometr. Intell. Lab. Syst., 96(2):132-143.

[10]Maestri, M.L., Cassanello, M.C., Hororwitz, G.I., 2009. Kernel PCA performance in processes with multiple operation modes. Chem. Prod. Process Model., 4(5), Article 7, p.1-6.

[11]Qin, S.J., Valle, S., Piovoso, M.J., 2001. On unifying multiblock analysis with application to decentralized process monitoring. J. Chemometr., 15(9):715-742.

[12]Voegtlin, T., 2005. Recursive principal components analysis. Neur. Networks, 18(8):1051-1063.

[13]Wang, X., Kruger, U., Lennox, B., 2003. Recursive partial least squares algorithms for monitoring complex industrial processes. Control Eng. Pract., 11(6):613-632.

[14]Zhang, Y., Qin, S.J., 2010. Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis. Acta Autom. Sin., 36(4):593-597.

[15]Zhou, D.H., Li, G., Qin, S.J., 2010. Total projection to latent structures for process monitoring. AIChE J., 56(1):168-178.

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