Aircraft Gas Turbine Engines Technical Condition Identification System

In this paper is shown that the probability-statistic methods application, especially at the early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence is considered the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods. Training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients' changes. Researches of skewness and kurtosis coefficients values’ changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes’ dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values’ changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stage-bystage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made. Keywords—Gas turbine engines, neural networks, fuzzy logic, fuzzy statistics. NOMENCLATURE H flight altitude [m] M Mach number * H T atmosphere temperature [ C] * H p atmosphere pressure [Pa] LP n low pressure compressor speed (RPM) [%] * 4 T exhaust gas temperature [ C] T G fuel flow [kg/h] Authors are with the National Academy of Aviation, AZ1045, Azerbaijan, Baku, Bina, 25th km (phone: 994-12-453-11-48; Fax: 994-12497-28-29; e-mail: [email protected]). T p fuel pressure [kg/cm] M p oil pressure [kg/cm] M T oil temperature [C] BS V back support vibration [mm/s] FS V forward support vibration [mm/s] ,.. , , 3 2 1 a a a regression coefficients in initial linear multiple regression equation of GTE condition model ,. , , 3 2 1 a a a ′ ′ ′ regression coefficients in actual linear multiple regression equation of GTE condition model ,.. ~ , ~ , ~ 3 2 1 a a a fuzzy regression coefficients in linear multiple regression equation of GTE condition model Y X ~ , ~ measured fuzzy input and output parameters of GTE condition model Y X r , correlation coefficients between GTE work parameters Y X r , ~ fuzzy correlation coefficients between GTE work parameters ⊗ fuzzy multiply operation ini initial act actual