Complex Identification Technique of Aircraft Gas Turbine Engine’s Health

Pashayev, A. M.; Sadiqov, R.; Abdullayev, P. S.

doi:10.1115/gt2003-38704

Cited by 5 publications

(8 citation statements)

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“…This allowance affects the GTE technical condition monitoring reliability and causes of the error decision in the GTE diagnostic and operating process [1][2][3] (Fig.1).Experimental investigation conducted by manual records shows that at the beginning of monitoring during 40÷60 measurements accumulated values of recorded parameters of good working order GTE aren't normal distribution.…”

Section: Abstract-gasmentioning

confidence: 99%

“…If for all training pairs, deviation value Е is less than given then training (correction) parameters of the network comes to end [1][2][3]. In opposite case it continues until value Е reach minimum.…”

Section: Abstract-gasmentioning

confidence: 99%

“…Correction of network parameters for left and right part is carried out as follows: The structure of NN for identification of the equation (1) parameters is given in [1][2][3].…”

Section: Abstract-gasmentioning

confidence: 99%

“…Therefore, it is necessary to note that correct application of fuzzy correlation-regression approach demands the analysis of fuzzy characteristics of GTE work parameters distributions. With this purpose should be carried out fuzzy analysis of distribution character of GTE work parameters on the basis of fuzzy values of skewness and kurtosis coefficients under the following formulas [1][2][3] ( ) ( ) …”

Section: Gte Condition Monitoring By Fuzzy Statistical Analysis mentioning

confidence: 99%

“…At the preliminary stage, when number of measurements is 60 ≤ N , GTE technical condition is described by the fuzzy linear regression equation (1). Identification of fuzzy linear model of GTE is made with help of NN which structure is given in [1][2][3]. Thus as the output parameter of GTE model is accepted the GTE exhaust gas temperature * 11 10 9 8 7 6 5 4…”

Section: Gte Condition Monitoring Using Complex Analysis Of First mentioning

confidence: 99%

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Aircraft Gas Turbine Engines’ Technical Condition Identification System

Abdullayev

Pashayev

Askerov

et al. 2005

Volume 1: Turbo Expo 2005

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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.

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Section: Abstract-gasmentioning

confidence: 99%

Section: Abstract-gasmentioning

confidence: 99%

“…Correction of network parameters for left and right part is carried out as follows: The structure of NN for identification of the equation (1) parameters is given in [1][2][3].…”

Section: Abstract-gasmentioning

confidence: 99%

Section: Gte Condition Monitoring By Fuzzy Statistical Analysis mentioning

confidence: 99%

Section: Gte Condition Monitoring Using Complex Analysis Of First mentioning

confidence: 99%

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Aircraft Gas Turbine Engines’ Technical Condition Identification System

Abdullayev

Pashayev

Askerov

et al. 2005

Volume 1: Turbo Expo 2005

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Methodology of Complex Diagnosing System for Aviation GTE

Abdullayev¹

2008

44th AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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

et al. 2008

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In this paper, it is shown that the use of probability‐statistic methods, especially at the early stage of diagnosing the technical condition of aviation gas turbine engines (GTE) when the flight information has fuzzy and limitation and uncertainty properties, is unfounded. Hence the efficiency of the use of Soft Computing methods‐fuzzy logic and neural networks at these diagnostic stages is considered. 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 to make a more adequate model of the technical condition of GTE, the dynamics changes of skewness and kurtosis coefficients are analysed. Research of skewness and kurtasis coefficients shows, that the statistical distributions of the work parameters of GTE have a fuzzy character. Hence, consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics of the changes in the dynamics of the work parameters of GTE allows to draw the conclusion that it is necessary to use fuzzy statistical analysis during the preliminary identification of the technical condition of engines. Research of changes in the values of correlation coefficients also demonstrates their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. The fuzzy multiple correlation coefficient of fuzzy multiple regression is considered for checking the adequacy of models. 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 nonlinear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The system that is developed to monitor the condition of GTE provides stage‐by‐stage estimation of the technical condition of an engine. As an application of this technique, an estimation of the new operating aviation engine temperature condition was made. Santrauka Straipsnyje atskleidžiamas tikimybinio-statistinio metodo nepagrįstumas diagnozuojant dujų turbininius variklius, kai informacija yra netiksli, ribota ir neapibrėžta. Parodytas technologijos Soft Computing taikymo efektyvumas. Taikant netikslios statistikos, netikslios logikos ir neuroninių tinklų tikslius metodus dujų turbininių variklių diagnozavimui atliekamas daugiamačių tiesinių ir netiesinių modelių (regresijos lygčių), gautų iš netikslių statistinių duomenų, apmokymas. Taikant aprašytą metodą buvo atlikta pradėto eksploatuoti turbininio variklio šiluminės būsenos analizė.

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Complex Identification Technique of Aircraft Gas Turbine Engine’s Health

Abstract: The new approach to identification of the aviation GTE technical condition is considered (examined) at an fuzzy, limitation and uncertainty of the information. This approach is based on applicability of fuzzy logic and artificial neural networks (Soft computing).

Cited by 5 publications

References 0 publications

Aircraft Gas Turbine Engines’ Technical Condition Identification System

Aircraft Gas Turbine Engines’ Technical Condition Identification System

Methodology of Complex Diagnosing System for Aviation GTE

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

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