Aircraft Gas Turbine Engines’ Technical Condition Identification System

Abstract

Abstract-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.

Identification System for the Technical Condition of Gas Turbine Engines of Aircraft

“…The basic characteristics of correlation coefficients (Fig 6; Appendix) show the necessity of using fuzzy NN for the processing of flight information. In that case correct application of this approach on describing up of the GTE technical condition changes is possible by fuzzy linear or non-linear model (Pashayev et al 2004a, Abdullayev et al 2005.…”

“…These models are made for each correct subcontrol engine of the fleet during the initial period of operation. On the basis of the analysis of the values of the regression coefficient (coefficients of influence) of all engine's multiple regression models with the help of mathematical statistics base and admissible range of coefficients (Abdullayev et al 2005, Pashayev et al 2004b.…”

“…However, it should be noted that statistic data processing using the aforementioned method is conducted by the preliminary allowance of the recorded parameters meaning normal distribution. This allowance affects the reliability of the monitoring of the technical condition of GTE and causes of the error decision in the GTE diagnostic and operating process (Pashayev et al 2004a, Pashayev et al 2003, Abdullayev et al 2005.…”