Authors: Ines Jaffel Okba Taouali Mohamed Faouzi Harkat Hassani Messaoud
Publish Date: 2016/06/18
Volume: 88, Issue: 9-12, Pages: 3265-3279
Abstract
This paper proposes a new reduced kernel method for monitoring nonlinear dynamic systems on reproducing kernel Hilbert space RKHS Here the proposed method is a concatenation of two techniques proposed in our previous studies the reduced kernel principal component RKPCA Taouali et al Int J Adv Manuf Technol 2015 and the singular value decompositionkernel principal component SVDKPCA Elaissi et al ISA Trans 521 96–104 2013 The proposed method is entitled SVDRKPCA It consists at first to identify an implicit RKPCA model that approaches “properly” the system behavior and after that to update this RKPCA model by SVD of an incremented and decremented kernel matrix using a moving data window The proposed SVDRKPCA has been applied successfully for monitoring of a continuous stirred tank reactor CSTR as well as a Tennessee Eastman process TEP
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