Gas Turbine Fault Diagnosis using Random Forests

Authors:
Manolis Maragoudakis;Euripides Loukis;Panayotis-Prodromos Pantelides
Affiliations:
University of the Aegean, Department of Information and Communication Systems Engineering, Samos, Greece;University of the Aegean, Department of Information and Communication Systems Engineering, Samos, Greece;University of the Aegean, Department of Information and Communication Systems Engineering, Samos, Greece
Venue:
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Year:
2008

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Abstract

In the present paper, Random Forests are used in a critical and at the same time non trivial problem concerning the diagnosis of Gas Turbine blading faults, portraying promising results. Random forests-based fault diagnosis is treated as a Pattern Recognition problem, based on measurements and feature selection. Two different types of inserting randomness to the trees are studied, based on different theoretical assumptions. The classifier is compared against other Machine Learning algorithms such as Neural Networks, Classification and Regression Trees, Naive Bayes and K-Nearest Neighbor. The performance of the prediction model reaches a level of 97% in terms of precision and recall, improving the existing state-of-the-art levels achieved by Neural Networks by a factor of 1.5%--2%.