Random Forests Identification of Gas Turbine Faults

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%. Furthermore, emphasis is given on the pre-processing phase, where feature selection and outliers identification is carried out, in order to provide the basis of a high performance automated diagnostic system. The conclusions derived are of more general interest and applicability.