Identification of rear model of TV3-117 aircraft engine based on the basis of neuro-multi-functional technologies
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Дата
2019
ORCID
DOI
Науковий ступінь
Рівень дисертації
Шифр та назва спеціальності
Рада захисту
Установа захисту
Науковий керівник
Члени комітету
Видавець
Innovative technologies and scientific solutions for industries = Сучасний стан наукових досліджень та технологій в промисловості. – 2019. – № 1 (7). – С. 43-49
Анотація
Предметом статті є авіаційний двигун TV3-117 і методи визначення його технічного стану. У статті було вирішене завдання ідентифікації зворотної багаторежимної моделі авіаційного двигуна TV3-117 з використанням нейронних мереж.
The subject matter in the article is TV3-117 aircraft engine and methods of identification of its technical condition. The goal of the work is to develop methods for identifying the technical state of the aircraft engine TV3-117 on the basis of real-time neural network technologies. The following tasks were solved in the article: the task of identifying the reverse multi-mode model of the aircraft engine TV3-117 using neural networks. The following methods used are – methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained – The application of the neural network apparatus is effective in solving a large range of tasks: identifying the mathematical model of the aircraft engine TV3-117, diagnosing the condition, analyzing the trends, forecasting the parameters, etc., despite the fact that these tasks usually relate to the class difficultly formalized (poorly structured), neural networks are adequate and effective in the process of their solution. In the process of solving the task of identifying the mathematical model of the aircraft engine TV3-117 on the basis of neural networks, it was established that neural networks solve the problem of identification more precisely classical methods. Conclusions: It was established that the error of identification of the aircraft engine TV3-117 with the help of a neural network of type perceptron did not exceed 1.8 %; For the neural network of radial-basic function (RBF) – 4.6 %, whereas for the classical method (LSM) it makes about 5.7 % in the considered range of changes in engine operation modes. It was found that neural network methods are more robust to external perturbations: for noise level ζ = 0.01, the error of identification of aircraft engine TV3-117 with the use of perceptron has increased from 1.8 to 3.8 %; for the neural network RBF – from 4.6 to 5.7 %, and for the least squares method – from 5.7 to 13.93 %. In the process of solving the task of identifying the inverse multi-mode model of the aviation engine TV3-117 on its parameters on the basis of neural networks (perceptron and RBF) it was shown that their use allows for indirect measurement of the parameters of the flowing part of the engine at different modes of its operation: in the absence of noise – with an error of not more than 1,8 and 4,6 % respectively; in the presence of noise (ζ = 0,01) – with an error of not more than 3,8 and 5,7 % respectively. Application in these conditions of the least squares method (polynomial regression model of the 8th order) allows us to obtain the error value: in the absence of noise – no more than 5,7 %; in the presence of noise – no more than 13,93 %.
Предметом статьи является авиационный двигатель TV3-117 и методы определения его технического состояния. В статье была решена задача идентификации обратной многорежимной модели авиационного двигателя TV3-117 с использованием нейронных сетей.
The subject matter in the article is TV3-117 aircraft engine and methods of identification of its technical condition. The goal of the work is to develop methods for identifying the technical state of the aircraft engine TV3-117 on the basis of real-time neural network technologies. The following tasks were solved in the article: the task of identifying the reverse multi-mode model of the aircraft engine TV3-117 using neural networks. The following methods used are – methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained – The application of the neural network apparatus is effective in solving a large range of tasks: identifying the mathematical model of the aircraft engine TV3-117, diagnosing the condition, analyzing the trends, forecasting the parameters, etc., despite the fact that these tasks usually relate to the class difficultly formalized (poorly structured), neural networks are adequate and effective in the process of their solution. In the process of solving the task of identifying the mathematical model of the aircraft engine TV3-117 on the basis of neural networks, it was established that neural networks solve the problem of identification more precisely classical methods. Conclusions: It was established that the error of identification of the aircraft engine TV3-117 with the help of a neural network of type perceptron did not exceed 1.8 %; For the neural network of radial-basic function (RBF) – 4.6 %, whereas for the classical method (LSM) it makes about 5.7 % in the considered range of changes in engine operation modes. It was found that neural network methods are more robust to external perturbations: for noise level ζ = 0.01, the error of identification of aircraft engine TV3-117 with the use of perceptron has increased from 1.8 to 3.8 %; for the neural network RBF – from 4.6 to 5.7 %, and for the least squares method – from 5.7 to 13.93 %. In the process of solving the task of identifying the inverse multi-mode model of the aviation engine TV3-117 on its parameters on the basis of neural networks (perceptron and RBF) it was shown that their use allows for indirect measurement of the parameters of the flowing part of the engine at different modes of its operation: in the absence of noise – with an error of not more than 1,8 and 4,6 % respectively; in the presence of noise (ζ = 0,01) – with an error of not more than 3,8 and 5,7 % respectively. Application in these conditions of the least squares method (polynomial regression model of the 8th order) allows us to obtain the error value: in the absence of noise – no more than 5,7 %; in the presence of noise – no more than 13,93 %.
Предметом статьи является авиационный двигатель TV3-117 и методы определения его технического состояния. В статье была решена задача идентификации обратной многорежимной модели авиационного двигателя TV3-117 с использованием нейронных сетей.
Опис
Identification of rear model of TV3-117 aircraft engine based on
the basis of neuro-multi-functional technologies / S. Vladov, Yu. Shmelov, I. Derevyanko and oth. // Innovative technologies and scientific solutions for industries = Сучасний стан наукових досліджень та технологій в промисловості. – 2019. – № 1 (7). – С. 43-49. – DOI: https://doi.org/10.30837/2522-9818.2019.7.043.
Ключові слова
Техніка. Технічні науки. Machinery. Engineering. Техника. Технические науки, Наукові публікації. Scientific publications. Научные публикации, aircraft engine TV3-117, neural network, perceptron, radial-basic function, identification, авиационный двигатель TV3-117, нейронная сеть, персептрон, радиально-основная функция, идентификация, авіаційний двигуни TV3-117, нейронна мережа, перцептрон, радіально-основна функція, ідентифікація