Abstract
Classifier between states of “normal/high maintenance/defective” for oil-lubricated parts and units of D30KP/KP-2 aircraft gas turbine engines is developed. The classifier is based on “random forest” machine learning algorithm. It is trained on results of microwave plasma measurements of metallic admixture in oil filter wash samples of engines. Technical state for train set was determined earlier by expert method and was confirmed by factory disassembly study. Classifier result for states “normal/high maintenance/defective” matches expert method in 73 %, 52 %, 66 % respectively.
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