PDF(Pubmed)
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.
摘要：
本文的主要规定是直升机涡轴发动机热气动力参数积分信号的神经网络方法的开发和应用。这使您可以实时有效地纠正传感器数据，确保读数的高精度和可靠性。已经开发出一种神经网络，该神经网络集成了直升机涡轴发动机参数的闭环，这是基于滤波方法进行调节的。这使得实现几乎100％（0.995或99.5％）的准确性成为可能，并在280个训练周期后将损失函数降低到0.005（0.5％）。已经开发了一种基于闭环反向传播误差的神经网络训练算法，集成基于滤波方法调节的直升机涡轴发动机参数。它结合了提高验证集精度和控制过拟合，考虑到误差动力学，这保持了模型的泛化能力。自适应训练速率提高了对数据变化和训练条件的适应性，提高性能。已经在数学上证明,直升机涡轴发动机参数调节神经网络闭环积分采用滤波法,与传统滤波器(中值递归，递归和中位数)，显著提高效率。此外，这可以减少第一和第二类型的误差：与中值递归滤波器相比是2.11倍，是递归滤波器的2.89倍，与中值滤波器相比是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，通过改进的过滤方法和神经网络数据集成，确保飞机的高效和安全运行。这些进步为航空业开辟了新的前景,通过先进的数据处理技术提高直升机的运营效率和整体飞行安全。
