Helicopter Turboshaft Engine Residual Life Determination by Neural Network Method

Abstract

Розроблено нейромережевий метод визначення залишкового ресурсу турбовального двигуна вертольота, основою якого є ієрархічна система, яка представлена ​​у вигляді моделі нейронної мережі, що складається з чотирьох шарів, що визначає числове значення залишкового ресурсу.

A neural network method has been developed for helicopter turboshaft engine residual life determination, the basis of which is a hierarchical system, which is represented in neural network model form, consisting of four layers, which determines the numerical value of the residual life. To implement a hierarchical system, a justified multilayer perceptron is used. A multilayer perceptron training algorithm has been developed, which, by introducing an initial parameter to the output layer, yields a prediction accuracy of up to 99.3%, and the adaptive Adam training rate ensures an accuracy of up to 99.4% in helicopter turboshaft engine residual life determination. A method for constructing a degradation curve has been developed that takes into account both the parameter predictions and similarities with past patterns, allowing you to determine the range of possible values of the residual life estimate, with a probability of up to 95%. The article considers an example of solving the task of determining the thermally stressed state of helicopter turboshaft engine compressor turbine blades and assessing their residual life. A computational experiment was carried out to determine the residual life of helicopter turboshaft engine compressor turbine blades, and the results, with 160 training epochs, recorded an accuracy of 99.3%, with a reduction in losses from 2.5% to 0.5% thanks to training process optimization by applying an adaptive training rate. The comparative analysis results showed that use of the multilayer perceptron as a hierarchical system gives better results than the classical RBF network and the least squares method. The first and second types of error were reduced by 2.23 times compared to the RBF network and by 4.74 times compared to the least squares method.

Разработан нейросетевой метод определения остаточного ресурса вертолетного турбовального двигателя, основой которого является иерархическая система, представленная в виде нейросетевой модели, состоящей из четырех слоев, определяющей численное значение остаточного ресурса.