Variable Reconstruction Using RBF-NLPCA for Process Monitoring

Abstract

State reconstruction approach is very useful for the detennination of the number of components retained in the PCA (Principal component analysis) model and for fault isolation (Dunia, 1996). An extension of this approach based on a NLPCA (Non Linear PCA) model is described in this paper. The NLPCA model is obtained using two RBF (Radial basis Function) networks where the nonlinear transformations of the input variables (that characterize the nonlinear principal component analysis) are modeled as a linear sum of radially symmetric kernel functions. A simulation example is given to show the performances of the proposed approach.