Fault Detection of Continuous Casting Process Using Kernel PCA

Xiao Yan Wang

doi:10.4028/www.scientific.net/AMR.468-471.1706

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 468-471Fault Detection of Continuous Casting Process...

Fault Detection of Continuous Casting Process Using Kernel PCA

Abstract:

Viewing to monitoring the continuous casting process, a nonlinear fault detection method based on kernel principal component analysis (KPCA) was introduced. KPCA can efficiently compute principal components in high-dimensional feature spaces by means of integral operators and nonlinear kernel functions, which is to first map the input space into a feature space via nonlinear mapping and then to compute the principal components in that feature space. Based on T2 and SPE charts in feature space, principal component analysis(PCA)can be used to detect faults.. The simulation results show that the proposed approach effectively captured the nonlinear relationship in the process variables and showed superior process monitoring performance compared to linear PCA.

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Periodical:

Advanced Materials Research (Volumes 468-471)

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1706-1709

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.468-471.1706

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Online since:

February 2012

Authors:

Xiao Yan Wang

Keywords:

Continuous Casting, Fault Detection, Kernel Principal Component Analysis (KPCA), Nonlinear Process

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