Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring

In this research,a new fault detection method based on kernel independent component analysis (kernel ICA) is developed.Kernel ICA is an improvement of independent component analysis (ICA),and is different from kernel principal component analysis (KPCA) proposed for nonlinear process monitoring.The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques.I2 (the sum of the squared independent scores) and squared prediction error (SPE) charts are adopted as statistical quantities.The proposed monitoring method is applied to Tennessee Eastman process,and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.

In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.