Identification of Aircraft Gas Turbine Engine's Temperature Condition

Authors: Pashayev A., Askerov D., C. Ardil, Sadiqov R., Abdullayev P.

Abstract:

Groundlessness of application probability-statistic methods are especially shown at an early stage of the aviation GTE technical condition diagnosing, when the volume of the information has property of the fuzzy, limitations, uncertainty and efficiency of application of new technology Soft computing at these diagnosing stages by using the fuzzy logic and neural networks methods. It is made training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis. At the information sufficiency it is offered to use recurrent algorithm of aviation GTE technical condition identification on measurements of input and output parameters of the multiple linear and nonlinear generalized models at presence of noise measured (the new recursive least squares method (LSM)). As application of the given technique the estimation of the new operating aviation engine D30KU-154 technical condition at height H=10600 m was made.

Keywords: Identification of a technical condition, aviation gasturbine engine, fuzzy logic and neural networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332774

References:

[1] Sadiqov R.A. Identification of the quality surveillance equation parameters //Reliability and quality surveillance.-M., 1999, Ôäû 6, p. 36- 39.
[2] Sadiqov R.A., Makarov N.V., Abdullayev P.S. V International Symposium an Aeronautical Sciences «New Aviation Technologies of the XXI century»//A collection of technical papers., section Ôäû4-Ôäû24, Zhukovsky, Russia, august, 1999.
[3] Pashayev A.M., Sadiqov R.A., Makarov N.V., Abdullayev P. S. Efficiency of GTE diagnostics with provision for laws of the distribution parameter in maintenance. Full-grown. VI International STC "Machine building and technosphere on border 21 century" //Collection of the scientific works// Org. Donechki Gov.Tech.Univ., Sevastopol, Ukraine, september, 1999, p.234-237.
[4] Ivanov L.A. and etc. The technique of civil aircraft GTE technical condition diagnosing and forecasting on registered rotor vibrations parameters changes in service.- M: GOS NII GA, 1984.- 88p.
[5] Doroshko S.M. The control and diagnosing of GTE technical condition on vibration parameters. - M.: Transport, 1984.-128 p.
[6] Abasov M.T., Sadiqov A.H., Aliyarov R.Y. Fuzzy neural networks in the system of oil and gas geology and geophysics // Third International Conference on Application of Fuzzy Systems and Soft computing/ Wiesbaden, Germany, 1998,- p.108-117.
[7] Yager R.R., Zadeh L.A. (Eds). Fuzzy sets, neural networks and soft computing. VAN Nostrand Reinhold. N.-Y. - Ôäû 4,1994.
[8] Mohamad H. Hassoun. Fundamentals of artificial neutral networks / A Bradford Book. The MIT press Cambridge, Massachusetts, London, England, 1995.
[9] Pashayev A.M., Sadiqov R.A., Makarov N.V., Abdullayev P.S. Estimation of GTE technical condition on flight information//Abstracts of XI All-Russian interinstit.science-techn.conf. "Gaz turbine and combined installations and engines" dedicated to 170 year MGTU nam. N.E.BAUMAN, sec. 1. N.E.BAUMAN MGTU., 15-17 november., Moscow.-2000.- p.22-24.
[10] Granovskiy V.A. and Siraya T.N. Methods of experimental-data processing in measurements
[in Russian], Energoatomizdat, Moscow, 1990.
[11] Greshilov A.A., Analysis and Synthesis of Stochastic Systems. Parametric Models and Confluence Analysis (in russian), Radio i Svyaz, Moscow, 1990.
[12] Pugachev V.S., Probability theory and mathematical statistics (in russian), Nauka, Moscow, 1979.