Stacking dynamic time warping for the diagnosis of dynamic systems

This paper explores an integrated approach to diagnosis of complex dynamic systems. Consistency-based diagnosis is capable of performing automatic fault detection and localization using just correct behaviour models. Nevertheless, it may exhibit low discriminative power among fault candidates. Hence, we combined the consistency based approach with machine learning techniques specially developed for fault identification of dynamic systems. In this work, we apply Stacking to generate time series classifiers from classifiers of its univariate time series components. The Stacking scheme proposed uses K-NN with Dynamic Time Warping as a dissimilarity measure for the level 0 learners, and Naive Bayes at level 1. The method has been tested in a fault identification problem for a laboratory scale continuous process plant. Experimental results show that, for the available data set, the former Stacking configuration is quite competitive, compare to other methods like tree induction, Support Vector Machines or even K-NN and Naive Bayes as stand alone methods.