Fuzzy Condition Monitoring System for Aviation Gas Turbine Engines

Abstract

In this paper is shown the efficiency of the new Soft Computing technology application at different diagnosing stages of aviation gas turbine engine (GTE) technical condition with using Fuzzy Logic and Neural Networks methods, when the flight information has property of a fuzzy, limitation and uncertainty. On the fuzzy statistical data basis and with high accuracy is made the training of Fuzzy Multiple Linear and Non-Linear models (Fuzzy Regression Equations). 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. With a view of completeness of GTE technical condition diagnosing in this paper are considered Fuzzy Thermodynamic Models. As output parameter of these models the outlet gas temperature of gas turbine (turbine exhaust gas temperature -EGT) expediency is considered. In view of limitation of controllable parameters’ structure are used also semiempirical models. The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.