Identification Technique of Aircraft Gas Turbine Engine’s Health

Abstract

Groundlessness of probability-statistic methods application is shown, especially at an early stage of the aviation gas turbine engine (GTE) technical condition diagnosing, when the volume of the information has property of the fuzzy, limitation and uncertainty. Hence efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the fuzzy logic and neural networks methods is considered. Training with high accuracy of multiple linear and nonlinear models (the regression equations) received on the statistical fuzzy data basis is made. For models choice is offered the application of the fuzzy correlation analysis results. Dynamics of correlation coefficients changes is considered. At the information sufficiency it 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 nonlinear generalised models at presence of noise measured (the new recursive least squares method). As application of the given technique the estimation of the new operating aviation engine D-30KU-154 (aircraft Tu-154M) technical condition was made.