| Autor: |
Vladov S; Department of Scientific Work Organization and Gender Issues, Kremenchuk Flight College of Kharkiv National University of Internal Affairs, 17/6 Peremohy Street, 39605 Kremenchuk, Ukraine., Scislo L; Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Craków, Poland., Sokurenko V; Kharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine., Muzychuk O; Kharkiv National University of Internal Affairs, Ministry of Internal Affairs of Ukraine, 61080 Kharkiv, Ukraine., Vysotska V; Information Systems and Networks Department, Lviv Polytechnic National University, 12 Bandera Street, 79013 Lviv, Ukraine.; Institute of Computer Science, Osnabrück University, 1 Friedrich-Janssen-Street, 49076 Osnabrück, Germany., Osadchy S; Flight Operation and Flight Safety Department, Flight Academy of the National Aviation University, 1 Chobanu Stepana Street, 25005 Kropyvnytskyi, Ukraine., Sachenko A; Research Institute for Intelligent Computer Systems, West Ukrainian National University, 11 Lvivska Street, 46009 Ternopil, Ukraine.; Department of Teleinformatics, Kazimierz Pulaski University of Radom, 29, Malczewskiego Street, 26-600 Radom, Poland. |
| Abstrakt: |
The article's main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies. |
