IDENTIFICATION OF REAR MODEL OF TV3-117 AIRCRAFT ENGINE BASED ON THE BASIS OF NEURO-MULTI-FUNCTIONAL TECHNOLOGIES

Keywords: aircraft engine, neural network, perceptron, radial-basic function, identification

Abstract

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 σ = 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 (σ = 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 %.

Downloads

Download data is not yet available.

Author Biographies

Serhii Vladov, Kremenchuk Flight College of National Aviation University
PhD (Engineering Sciences), Head of Organization of Scientific Activities, Licensing and Accreditation Laboratory, Teacher at the Department of Energy Supply and Control Systems
Yurii Shmelov, Kremenchuk Flight College of National Aviation University
PhD (Engineering Sciences), Deputy College Chief for Curriculum, Teacher at the Department of Energy Supply and Control Systems
Ivan Derevyanko, Kremenchuk Flight College of National Aviation University
Teacher at the Department of Aviation Transport
Inna Dieriabina, Kremenchuk Flight College of National Aviation University
Teacher at the Department of Aviation Transport
Liudmyla Chyzhova, Kremenchuk Flight College of National Aviation University
Teacher at the Department of Ukrainian and Foreign Languages

References

Shevchenko, I., Tertyshnyi, V. and Koval, S. (2017), "Designing a model of a decision support system based on a multi-aspect factographic search", Eastern-European Journal of Enterprise Technologies, Vol. 4, Issue 2 (88), P. 20–26. DOI: https://doi.org/10.15587/1729-4061.2017.108569

Pashayev, A. M., Askerov, D. D., Ardil, C., Sadiqov, R. A., Abdullayev, P. S. (2007), "Complex Condition Monitoring System of Aircraft Gas Turbine Engine", International Journal of Aerospace and Mechanical Engineering, Vol. 1, No. 11, P. 689–695.

Stamatis, A. G. (2011), "Evaluation of gas path analysis methods for gas turbine diagnostics", Journal of Mechanical Science and Technology, Vol. 25, Issue 2, P. 469–477.

Ntantis, E. L. (2015), "Diagnostic Methods for an Aircraft Engine Performance", Journal of Engineering Science and Technology, Review 8 (4), P. 64–72.

Kiakojoori, S., Khorasani, K. (2016), "Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis", Neural Computing & Applications, Vol. 27, No. 8, P. 2151–2192.

Mohammadi, R., Naderi, E., Khorasani, K., Hashtrudi-Zad, S. (2011), "Fault diagnosis of gas turbine engines by using dynamic neural networks", 2011 IEEE International Conference on Quality and Reliability, Bangkok, Thailand, P. 25–30.

Zhernakov, S. V., Vasilev, V. I., Musluhov, I. I. (2009), "Onboard algorithms for monitoring parameters of gas turbine engines based on neural network technology" ["Bortovyie algoritmyi kontrolya parametrov GTD na osnove tehnologii neyronnyih setey"], Bulletin of USATU, Vol. 12, No. 1 (30), P. 61–74.

Zhernakov, S. V., Ravilov, R. F. (2011), "Identification of the reverse multimode GTE model by the parameters of its oil system based on neural network technology" ["Identifikatsiya obratnoy mnogorezhimnoy modeli GTD po parametram ego maslyanoy sistemyi na osnove tehnologii neyronnyih setey"], Bulletin of Kalashnikov ISTU, No. 3 (51), P. 126–129.

Zhernakov, S. V. (2007), "Neural network technology for diagnosing the technical condition of aircraft engines" ["Neyrosetevyie tehnologii dlya diagnostiki tehnicheskogo sostoyaniya aviatsionnyih dvigateley"], Information Technology, No. 8, P. 22–29.

Zhernakov, S. V., Vasilev, V. I. (2009), "Identification of GTE characteristics based on neural network technology" ["Identifikatsiya harakteristik GTD na osnove tehnologii neyronnyih setey"], Kontrol’. Diagnostika ("Testing. Diagnostics"), No. 2, P. 54–61.

Shmelоv, Yu., Vladov, S., Kryshan, O., Gvozdik, S. and Chyzhova, L. (2018), "Research of classification method of TV3-117 engine ratings operations based on neural network technologies", Innovative Technologies and Scientific Solutions for Industries, No. 4 (6), P. 93–101. DOI: https://doi.org/10.30837/2522-9818.2018.6.093

Castro, W., Oblitas, J., Santa-Cruz, R., Avila-George, H. (2017), "Multilayer perceptron architecture optimization using parallel computing techniques", PLoS One, Vol. 12 (12): e0189369, 17 p.

Que, Q., Belkin, M. (2016), "Back to the Future: Radial Basis Function Networks Revisited", Appearing in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, JMLR : W&CP, Vol. 51, 13 p.

Vladov, S. I. Klimova, Ya. R. (2018), "Application of the adaptive training method of the neural network for diagnostics of the Mi-8MTV helicopter engine" ["Primenenie adaptivnogo metoda obucheniya neyronnoy seti dlya diagnostiki dvigatelya vertoleta Mi-8MTV"], Information Technologies: Science, Technology, Technology, Education, Health (MicroCAD-2018), May 16–18, 2018, Kharkiv, Part 1, P. 14.

Shmelev, Yu. N., Vladov, S. I., Boyko, S. N., Klimova, Ya. R., Vishnevskiy, S. Ya. (2018), "Diagnostics of the state of the Mi-8MTV helicopter engine using neural networks" ["Diagnostika sostoyaniya dvigatelya vertoleta Mi-8MTV s primeneniem neyronnyih setey"], Bulletin of the Khmelnytsky National University, No. 3.2018, P. 165–170.


Abstract views: 15
PDF Downloads: 14
Published
2019-03-22
How to Cite
Vladov, S., Shmelov, Y., Derevyanko, I., Dieriabina, I. and Chyzhova, L. (2019) “IDENTIFICATION OF REAR MODEL OF TV3-117 AIRCRAFT ENGINE BASED ON THE BASIS OF NEURO-MULTI-FUNCTIONAL TECHNOLOGIES”, INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (1 (7), pp. 43-50. doi: 10.30837/2522-9818.2019.7.043.