Using machine learning for technical condition assessment of the frictions units of gas-turbine engines by the results of the microwave plasma measurements
The paper considers the features of microwave spectral method regarding the measurement of wear particle parameters in samples of GTE oil system. The principles behind microwave spectral method and measured parameters are described. The oil sample and filter wash sample diagnostic features that are used for evaluation of technical state are described. Classifier between states of "normal", "high maintenance", "defective" for oil-lubricated parts and units of D30KP/KP-2 aircraft gas turbine engines was developed. The classifier is based on "random forest" machine learning algorithm. It’s 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% of the cases, respectively. Methods for increasing the classification accuracy are proposed, such as error decrease of measurement of complex composition particles and revision of the expert evaluation with subsequent algorithm retraining.