IDENTIFICATION OF REAR MODEL OF TV3-117 AIRCRAFT ENGINE BASED ON THE BASIS OF NEURO-MULTI-FUNCTIONAL TECHNOLOGIES
DOI:
https://doi.org/10.30837/2522-9818.2019.7.043Keywords:
aircraft engine, neural network, perceptron, radial-basic function, identificationAbstract
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 %.
References
1. 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
2. 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.
3. 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.
4. Ntantis, E. L. (2015), "Diagnostic Methods for an Aircraft Engine Performance", Journal of Engineering Science and Technology, Review 8 (4), P. 64–72.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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
12. 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.
13. 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.
14. 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.
15. 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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Serhii Vladov, Yurii Shmelov, Ivan Derevyanko, Inna Dieriabina, Liudmyla Chyzhova
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
