Artificial Intelligence
Characterizing diagnoses and systems
Readings in model-based diagnosis
Original Contribution: Stacked generalization
Neural Networks
The consistency-based approach to automated diagnosis of devices
Principles of knowledge representation
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Inference for the Generalization Error
Machine Learning
Ensembles of nested dichotomies for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Decomposition methodology for classification tasks: a meta decomposer framework
Pattern Analysis & Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Stacking Dynamic Time Warping for the Diagnosis of Dynamic Systems
Current Topics in Artificial Intelligence
Temporal decision trees: model-based diagnosis of dynamic systems on-board
Journal of Artificial Intelligence Research
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Possible conflicts: a compilation technique for consistency-based diagnosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.