Onboard Parameter Identification Method of the Tv3-117 Aircraft Engine of the Neural Network Technologies

Abstract

Purpose. The work is devoted to the development of an on-board method for identifying the parameters of TV3-117 aircraft engine based on neural network technology. The solution of the problem of identifying a model of TV3-117 engine in onboard conditions by classical methods, including the method of least squares and approximation by cubic splines, and neural network, by building a neural network according to source data. Methodology. The work is based on the methods of probability theory and mathematical statistics, neuroinformatics, information systems theory and data processing. Two neural network architectures were used in the work: a perceptron-type neural network and a radial basis function neural network. Results. A method was developed for determining the optimal structure of a neural network, which consists in determining the neural network architecture, choosing the optimal algorithm for finding weights of neurons and teaching a neural network, analyzing the effectiveness of various neural network training algorithms, determining the structure of a neural network, which consists in finding the minimum error of neural network training depending on the number of neurons in the hidden layer, as well as in the analysis of the effectiveness of the results. It has been established that neural networks solve the identification problem more precisely than classical methods, since the error of identification at the output of the perceptron architecture neural network is 1.6 times less than in the regression model developed on the basis of the least square's method for the considered range of engine operating modes. Originality. The scientific novelty of the results obtained is as follows: For the first time, a method was developed for determining the optimal structure of a neural network, which made it possible to solve the problem of identifying a model of an aircraft engine, the TV3-117, in onboard conditions with minimal errors. The method of identifying the technical condition of the TV3-117 aircraft engine in onboard conditions, which differs from the existing ones due to the use of neural network technologies, makes it possible to increase the reliability of monitoring and diagnostics of the technical condition of the TV3-117 aircraft engine under its flight conditions. Practical value. The developed neural network can be one of the units of the expert system that can automatically make decisions regarding the technical condition of the aviation engine TV3-117 in flight modes and provide information to the crew about the possibility of further safe movement of the aircraft. The task of developing an expert system can be effectively solved using the mathematical apparatus of neural networks, since its use increases the reliability and accuracy of classification of modes, identification, control, diagnostics, time series analysis (forecasting), debugging of engine parameters, etc., which will increase reliability of obtaining the necessary results.

Нейроеволюційний Метод Колокації Розв’язання Диференціальних Рівнянь

“…Використання нейронних мереж у інженерних задачах включає прогнозування та оптимізацію, керування та автоматизацію, обробку сигналів та візуальне розпізнавання, проєктування та імітацію, а також дослідження та розробку нових технологій. Так, наприклад, робота [1] містить розв'язання задачі ідентифікації параметрів авіаційного двигуна ТВ3-117 у бортових умовах із застосуваннях нейронних мереж. У загальному випадку, в інженерних застосуваннях, зазвичай, використовуються математичні моделі, які описуються диференціальними рівняннями як лінійними, так і нелінійними [2].…”

Methodology for Control of Helicopters Aircraft Engines Technical State in Flight Modes Using Neural Networks

“…Accordingly, it becomes necessary to train / retrain the newly created neural network. Then, for the convenience of further use and displaying the graphical representation, the transition to the reduced thermogasdynamic parameters [15,16]. This stage is necessary for human control of the neural network, in the case when there is an additional stage of control.…”

Алгоритми Діагностики Та Парирування Відмов Каналів Вимірювання Системи Автоматичного Управління Авіаційного Двигуна Тв3-117 У Польотних Режимах На Основі Нейромережевих Технологій

“…Let's consider the problem of identifying the characteristics of aviation engine on steady modes of its operation. In these modes, the engine is described by the equations of the form [5]:…”

“…The choice of neural network topology is described in detail in [5]. For the solution of the problem of identification the multi-model aircraft engine TV3-117 in neural basis as basic architectures were selected and perceptron neural networks radial basis function (RBF).…”