Model-Plant Mismatch Detection with Support Vector Machines

Abstract We propose a model-plant mismatch (MPM) detection strategy based on a novel closed-loop identification approach and one-class support vector machine (SVM) learning technique. With this scheme we can monitor MPM and noise model change separately, thus discriminating the MPM from noise model change. Another advantage of this approach is that it is applicable to routine operating data that may lack any external excitations. Theoretical derivations on the closed-loop identification method are provided in this paper, showing that it can furnish a consistent parameter estimate for the process model even in the case where a priori knowledge about the true noise model structure is not available. We build an SVM model based on process and noise model estimates from training data to predict the occurrence of MPM in the testing data. An example on paper machine control is provided to verify the proposed MPM detection framework.