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Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA
A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(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 fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA.
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Journal of Donghua University(English Edition)
2007年05期
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