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.

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Purpose. Construction of a mathematical model of the aircraft engine TV3-117 based on the results of observations of its reaction to environmental disturbances. The solution of the problem of identifying a dynamic 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. In this paper, the Elman recurrent network with one hidden layer with a sigmoid neuron activation function with feedback was applied. 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. The ability of the developed neural network to smooth out white noise was proved by determining the identification error of the rotational speed of the turbocharger's rotor, which was 0,005 % and did not exceed the limit-permissible value of 0,5 %. 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 dynamic 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. References 10, table 1, figure 4.

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