Abstract
Abstract In this paper, an approach for process monitoring using a multivariate statistical technique, namely kernel principal component analysis is studied. Kernel principal analysis has recently been proposed as a new method for performing a nonlinear form of principal component analysis (PCA). The basic idea of kernel PCA is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. For the process monitoring application, reconstructed input patterns can be obtained by approximating the pre-image of scores in feature space. An application study of an electro-pneumatic valve actuator in a sugar factory is described. The results show that the kernel PCA approach can detect several actuator faults earlier than linear PCA This study indicates the great potential of Kernel PCA for process monitoring.
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