A theory of diagnosis from first principles
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
An Alternative Approach to Dependeny-Recording Engines in Consistency-Based Diagnosis
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
Lessons Learned from Diagnosing Dynamic Systems Using Possible Conflicts and Quantitative Models
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Exact indexing of dynamic time warping
Knowledge and Information Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
"Physical negation": integrating fault models into the general diagnostic engine
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Ensemble methods and model based diagnosis using possible conflicts and system decomposition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Wind turbines fault diagnosis using ensemble classifiers
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
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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.