Abstract
This work presents an on-line diagnosis algorithm for dynamic systems that combines model based diagnosis and machine learning techniques. The Possible Conflicts (PCs) method is used to perform consistency based diagnosis, providing fault detection and isolation. Machine learning methods are use to induce time series classifiers, that are applied on line for fault identification. The main contribution of this work is that Possible Conflicts are used to decompose the physical system, defining the input-output structure of an ensemble of classifiers. Experimental results on a simulated pilot plant show that the ensemble created from PCs decomposition has an important potential to increase the accuracy of individual classifiers for several learning algorithms. Without PCs decomposition, the best results were for another ensemble method, Stacking. These results are improved when combining Stacking with PCs decomposition.
This work was supported by the Project TIN2009-11326 of the Spanish Ministry of Science and Innovation.
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Alonso-González, C.J., Rodríguez, J.J., Prieto, Ó.J., Pulido, B. (2010). Ensemble Methods and Model Based Diagnosis Using Possible Conflicts and System Decomposition. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13025-0_13
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