Vladov Serhii, Scislo Lukasz, Sokurenko Valerii, Muzychuk Oleksandr, Vysotska Victoria, Osadchy Serhii, Sachenko Anatoliy
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.
Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Craków, Poland.
Sensors (Basel). 2024 Jun 29;24(13):4246. doi: 10.3390/s24134246.
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.
本文的主要内容是开发并应用一种神经网络方法，用于直升机涡轮轴发动机热燃气动力学参数的综合信号处理。这使得能够实时有效地校正传感器数据，确保读数的高精度和可靠性。已开发出一种神经网络，该网络集成了直升机涡轮轴发动机参数的闭环，这些闭环基于滤波方法进行调节。这使得实现近100%（0.995或99.5%）的精度成为可能，并在280个训练周期后将损失函数降低到0.005（0.5%）。基于闭环反向传播中的误差，开发了一种用于神经网络训练的算法，该算法集成了基于滤波方法调节的直升机涡轮轴发动机参数。它结合了提高验证集精度和控制过拟合，同时考虑误差动态，从而保留了模型的泛化能力。自适应训练率提高了对数据变化和训练条件的适应性，提升了性能。从数学上证明，与传统滤波器（中值递归、递归和中值滤波器）相比，使用滤波方法调节神经网络闭环集成的直升机涡轮轴发动机参数显著提高了效率。此外，这使得第一类和第二类误差得以降低：与中值递归滤波器相比降低了2.11倍，与递归滤波器相比降低了2.89倍，与中值滤波器相比降低了4.18倍。所取得的成果显著提高了直升机涡轮轴发动机传感器读数的准确性（高达99.5%）和可靠性，通过改进的滤波方法和神经网络数据集成确保了飞机的高效和安全运行。这些进展为航空业开辟了新的前景，通过先进的数据处理技术提高了运营效率和直升机的整体飞行安全性。
