RESEARCH OF CLASSIFICATION METHOD OF TV3-117 ENGINE RATINGS OPERATIONS BASED ON NEURAL NETWORK TECHNOLOGIES

Keywords: engine, neural network, perceptron, engine ratings, classification

Abstract

The subject matter of the article is ТV3-117 engine ratings and recognition methods. The goal of the work is to create methods for classification TV3-117 engine ratings based on neural network technologies in real time. The following tasks were solved in the article: the principles formation on classification and recognition of TV3-117 engine’s conditions, determination of main steps for solving problem of classification and recognition TV3-117 engine conditions in the neural network basis, development of a method for the classification and recognition TV3-117 engine conditions using neural networks. The following methods used are – methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the information systems theory and data processing. The following results were obtained – the principles of classification and recognition TV3-117 engine conditions are formulated and the main steps for solving this problem are defined. It is substantiated that solving the problem of classifying the TV3-117 engine ratings in the neural network basis allows solve this problem more efficiently with less time and computational resources than using classical methods (for example, the Bayes method). Conclusions: using the neural network technologies for the classification and recognition the TV3-117 engine conditions allows to reduce the processing time, and most of the time spent on solving this problem is used to train the neural network. Prospects for further research are the development of an expert system, one of the modules is the module of classification and recognition TV3-117 engine conditions which is used in the board system to monitor and diagnose the engine technical condition and interact with the engine control systems, allows is to effect to the executive mechanism fluently and in time, from the one hand, to improve the quality control engine and its subsystems from the other hand in order to increase its reliability during its operation.

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Author Biographies

Юрій Миколайович Шмельов, Kremenchuk Flight College of National Aviation University
PhD (Engineering Sciences), Kremenchuk Flight College of National Aviation University. Deputy College Chief for Curriculum, Teacher at the Department of Energy Supply and Control Systems
Сергій Ігорович Владов, Kremenchuk Flight College of National Aviation University
PhD (Engineering Sciences), Kremenchuk Flight College of National Aviation University. Head of Organization of Scientific Activities, Licensing and Accreditation Laboratory, Teacher at the Department of Energy Supply and Control Systems
Олексій Федорович Кришан, Kremenchuk Flight College of National Aviation University
PhD (Economics Sciences), Kremenchuk Flight College of National Aviation University, Dean of Faculty of Aviation Transport, Electricity and Management, Teacher at the Department of Management and Administration
Станіслав Денисович Гвоздік, Kremenchuk Flight College of National Aviation University
Kremenchuk Flight College of National Aviation University, Dean of Faculty of Aviation Transport, Teacher at the Department of Aviation Transport
Людмила Іванівна Чижова, Kremenchuk Flight College of National Aviation University
Kremenchuk Flight College of National Aviation University, Teacher at the Department of Ukrainian and Foreign Languages

References

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.

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.

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.

O’Hagan, A. (2008), "The Bayesian Approach to Statistics", Handbook of Probability: Theory and Applications, P. 85–100.

Bodyanskiy, E. V., Teslenko, N. O., Deyneko, A. O. (2011), "An evolutionary neural network with nuclear activation functions and an adaptive algorithm for its training" ["EvolyutsIyna neyronna merezha z yadernimi funktsIyami aktivatsiyi y adaptivniy algoritm yiyi navchannya"], Scientific works. Computer Technology, Issue 148, Vol. 160, P. 53–58.

Mansour, W., Ayoubi, R., Ziade, H., Velazco, R., EL Falou, W. (2011), "An optimal implementation on FPGA of a hopfield neural network", Advances in Artificial Neural Systems, Vol. 2011, P. 7:1–7:9.

Kohonen, T. (2013), "Essentials of the self-organizing map", Neural Networks, Vol. 37, P. 52–65.

Bodyanskiy, E. V., Vinokurova, O. A. (2007), "Robust learning algorithm for radial-basic adaptive phase-wavelet neural network" ["Robastniy algoritm navchannya radialno-bazisnoyi adaptivnoyi fazzi-veyvlet-neyronnoyi merezhi"], Adaptive automatic control systems, No. 11, P. 3–15.

Bodyanskiy, E. V., Deyneko, A. O., Deyneko, Zh. V., Shalamov, M. O. (2015), "Adaptive training of the neural network of reference vectors of least squares" ["Adaptivne navchannya neyronnoyi merezhi opornih vektorIv naymenshih kvadrativ"], Information and control systems on the railway transport, No. 2, P. 71–74.

Elfwing, S., Uchibe, E., Doya, K. (2018), "Sigmoid-weighted linear units for neural network function approximation in reinforcement learning", Neural Networks, Vol. 107, P. 3–11.

Yamanashi, Y., Umeda, K., Yoshikawa, N. (2013), "Pseudo Sigmoid Function Generator for a Superconductive Neural Network", IEEE Transactions on Applied Superconductivity, Vol. 23, Issue 3, P. 1701004.

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.

Vasilets, T. Yu., Varfolomiev, O. O., Tyutyun, R. V., Alforov, Yu. O., Vlasov, A. O. (2017), "Synthesis of the NN Predictive Controller for controlling a three-mass electromechanical system" ["Sintez neyroregulyatora NN Predictive Controller dlya upravlinnya trohmasovoyu elektromehanichnoyu sistemoyu"], Information processing systems, Issue 3 (149), P. 88–95.

Shmelov, Y., Vladov, S., Klimova, Y., Kirukhina, M. (2018), "Expert system for identification of the technical state of the aircraft engine TV3-117 in flight modes", System Analysis & Intelligent Computing: IEEE First International Conference on System Analysis & Intelligent Computing (SAIC), 08–12 October, Kiev, P. 77–82.


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Published
2018-12-17
How to Cite
Шмельов, Ю., Владов, С., Кришан, О., Гвоздік, С. and Чижова, Л. (2018) “RESEARCH OF CLASSIFICATION METHOD OF TV3-117 ENGINE RATINGS OPERATIONS BASED ON NEURAL NETWORK TECHNOLOGIES”, INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (4 (6), pp. 93-102. doi: 10.30837/2522-9818.2018.6.093.

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