ArticleSensors (Basel, Switzerland)2024
Neural Network Signal Integration from Thermogas-Dynamic Parameter Sensors for Helicopters Turboshaft Engines at Flight Operation Conditions.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Study of Tribological Characteristics on the Surface of Railway Transport Components Using Atmospheric Plasma.Materials (Basel, Switzerland) · 2026Article
- Neural Network Method of Analysing Sensor Data to Prevent Illegal Cyberattacks.Sensors (Basel, Switzerland) · 2025Article
- Investigation of Buckling and Failure in Thin-Walled Columns Fabricated from PLA and PETG Using FDM 3D Printing.Materials (Basel, Switzerland) · 2025Article
- Freight wagon body design with increased load capacity.Scientific reports · 2025Article
- Helicopter Turboshaft Engines' Neural Network System for Monitoring Sensor Failures.Sensors (Basel, Switzerland) · 2025Article
- Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
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Abstract
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.
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