Abstract
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 Naïve 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 Naïve Bayes as stand alone methods.
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Keywords
- Support Vector Machine
- Machine Learning Technique
- Dynamic Time Warping
- Fault Mode
- Inductive Logic Programming
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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Alonso, C.J., Prieto, O.J., Rodríguez, J.J., Bregón, A., Pulido, B. (2007). Stacking Dynamic Time Warping for the Diagnosis of Dynamic Systems. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_2
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