Novel Nonlinear Process Monitoring Based on KPCA-ICA

Ying Wang Xiao; Chen Zhong Zhang

doi:10.4028/www.scientific.net/AMR.588-589.1054

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 588-589Novel Nonlinear Process Monitoring Based on...

Novel Nonlinear Process Monitoring Based on KPCA-ICA

Abstract:

A novel nonlinear process monitoring method based on kernel principal component analysis (KPCA) - independent component analysis (ICA) is proposed. The new method is a two-phase algorithm: whitened KPCA plus ICA. KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel. ICA seeks the projection directions in the KPCA whitened space, making the distribution of the projected data as non-gaussian as possible. The application to the Tennessee Eastman (TE) simulated process indicates that the proposed process monitoring method can effectively capture the nonlinear relationship in process variables. Its performance significantly outperforms monitoring method based on ICA.

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Periodical:

Advanced Materials Research (Volumes 588-589)

Pages:

1054-1057

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.588-589.1054

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Online since:

November 2012

Authors:

Ying Wang Xiao, Chen Zhong Zhang

Keywords:

Independent Component Analysis (ICA), Kernel Principal Component Analysis, Nonlinear Process Monitoring

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References

[1] M.A. Bin Shams, H. M. Budman, T. A. Duever: Fault detection, identification and diagnosis using CUSUM based PCA, Chemical Engineering Science, Vol. 66(20) (2011), pp.4488-4498.

DOI: 10.1016/j.ces.2011.05.028

Google Scholar

[2] Hyvarinen A, Oja E: Independent Component Analysis: Algorithms and Applications, Neural Networks, Vol. 13(4) (2000), pp.411-430.

DOI: 10.1016/s0893-6080(00)00026-5

Google Scholar

[3] Lee J M, Yoo C K, Lee I B: Statistical Process Monitoring with Independent Component Analysis, Journal of Process Control, Vol. 14(5) (2004), pp.467-485.

DOI: 10.1016/j.jprocont.2003.09.004

Google Scholar

[4] Lee J M, Yoo C K, Choi S W: Nonlinear Process Monitoring Using Kernel Principal Component Analysis, Chemical Engineering Science, Vol. 59(1) (2004), pp.223-234.

DOI: 10.1016/j.ces.2003.09.012

Google Scholar

[5] Jidong Shao, Gang Rong, Jong Min Lee: Learning a data-dependent kernel function for KPCA-based nonlinear process monitoring, Chemical Engineering Research and Design, Vol. 87(11), (2009), p.1471–1480.

DOI: 10.1016/j.cherd.2009.04.011

Google Scholar