Automated defect detection in oil-lubricated parts and units of D30KP/KP-2 aircraft gas turbine engines by results of microwave plasma method

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 measure...

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Bibliographic Details
Published in:Journal of physics. Conference series 2019-11, Vol.1384 (1), p.12017
Main Authors: Hodunaev, A Y, Drokov, V G, Drokov, V V, Murishenko, V V
Format: Article
Language:English
Subjects:
Aircraft
Aircraft engines
Aircraft maintenance
Algorithms
Classifiers
Gas turbine engines
Machine learning
Microwave plasmas
Physics
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1384/1/012017