| 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%. |
|---|
Greek