Moving window KPCA with reduced complexity for nonlinear dynamic process monitoring. (September 2016) - Jaffel, Ines; Taouali, Okba; Harkat, Mohamed Faouzi; Messaoud, Hassani
Abstract: This paper proposes an improved Reduced Kernel Principal Component Analysis (RKPCA) for handling nonlinear dynamic systems. The proposed method is entitled Moving Window Reduced Kernel Principal Component Analysis (MW-RKPCA). It consists firstly in approximating the principal components (PCs) of the KPCA model by a reduced data set that approaches "properly" the system behavior in the order to elaborate an RKPCA model. Secondly, the proposed MW-RKPCA consists on updating the RKPCA model using a moving window. The relevance of the proposed MW-RKPCA technique is illustrated on a Tennessee Eastman process. Highlights: This paper proposes a new Reduced Kernel Principal Component Analysis (RKPCA) technique entitled Moving Window RKPCA (MW-RKPCA). The proposed MW-RKPCA provides lower computation time and memory complexity. It consists on using RKPCA technique to select a reduced set of observations that 'sufficiently' approaches the system behavior. The updating procedure of the RKPCA model is achieved using a moving window technique. The proposed MW-RKPCA algorithm has been evaluated on Tennessee Eastman process.