scholarly journals Aircraft Gas Turbine Engines’ Technical Condition Identification System

2005 ◽  
Author(s):  
P. S. Abdullayev ◽  
A. M. Pashayev ◽  
D. D. Askerov ◽  
R. A. Sadiqov
Keyword(s):  
Gas Turbine ◽  
Least Squares Method ◽  
Technical Condition ◽  
Fuzzy Regression ◽  
Recursive Least Squares ◽  
Identification System ◽  
Regression Equations ◽  
Adequate Model ◽  
Non Linear ◽  
Skewness And Kurtosis

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. 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-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine D-30KU-154 technical condition was made.

Fuzzy Statistics Approach for Aviation GTE Condition Estimation Technique

2006 ◽  
Author(s):  
P. S. Abdullayev
Keyword(s):  
Linear Models ◽  
Least Squares Method ◽  
Early Stage ◽  
Technical Condition ◽  
Multiple Correlation Coefficient ◽  
Fuzzy Regression ◽  
Recursive Least Squares ◽  
Regression Equations ◽  
Adequate Model ◽  
Skewness And Kurtosis

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 nonlinear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). 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.

scholarly journals IDENTIFICATION SYSTEM FOR THE TECHNICAL CONDITION OF GAS TURBINE ENGINES OF AIRCRAFT

Aviation ◽  
2008 ◽  
Vol 12 (4) ◽  
pp. 101-112 ◽  
Author(s):  
Arif Pashayev ◽  
Djakhangir Askerov ◽  
Ramiz Sadiqov ◽  
Parviz Abdullayev
Keyword(s):  
Gas Turbine ◽  
Soft Computing ◽  
Technical Condition ◽  
Recursive Least Squares ◽  
Turbine Engines ◽  
Identification System ◽  
Regression Equations ◽  
Gas Turbine Engines ◽  
Adequate Model ◽  
Skewness And Kurtosis

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.

Identification Technique of Aircraft Gas Turbine Engine’s Health

Volume 3 ◽  
2004 ◽  
Author(s):  
A. M. Pashayev ◽  
D. D. Askerov ◽  
R. A. Sadiqov ◽  
P. S. Abdullayev
Keyword(s):  
Gas Turbine ◽  
Least Squares Method ◽  
Early Stage ◽  
Nonlinear Models ◽  
New Technology ◽  
Correlation Coefficients ◽  
Technical Condition ◽  
Recursive Least Squares ◽  
Regression Equations ◽  
Linear And Nonlinear

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.

Fuzzy Condition Monitoring System for Aviation Gas Turbine Engines

2007 ◽  
Author(s):  
P. S. Abdullayev ◽  
A. M. Pashayev ◽  
R. A. Sadiqov ◽  
A. J. Mirzoyev
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Technical Condition ◽  
Regression Equations ◽  
Gas Temperature ◽  
Adequate Model ◽  
Technology Application ◽  
Condition Monitoring System ◽  
Skewness And Kurtosis

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.

scholarly journals Моделювання залежностей «багатовимірний вхід–вихід» для автоматизації процесів керування в умовах невизначеності

2015 ◽  
pp. 86-90
Author(s):  
Л. І. Лєві
Keyword(s):  
Least Squares Method ◽  
Statistical Processing ◽  
Fuzzy Regression ◽  
Regression Equations ◽  
Continuous Output ◽  
Fuzzy Regression Model ◽  
Multivariate Dependence ◽  
Input Variables ◽  
Definition Of

Розглянута у роботі технологія дає змогу шляхомпоєднання переваг м’яких обчислень і реґресійного ана-лізу будувати багатофакторні залежності з неперерв-ним виходом, враховуючи як можливість визначенняступеня важливості вхідних змінних, так і їх взаємодійнеобхідного порядку. Проте під час моделюванняоб’єктів із неперервним виходом, коли необхідна до-статня точність визначення чіткого значення вихідноївеличини, знаходження параметрів нечіткого рівнянняреґресії за методом найменших квадратів та парамет-рів функцій належностей шляхом статистичної оброб-ки експертної інформації не може в повній мірі забез-печити потрібну точність. Для цього потрібно налаш-тувати за навчальною вибіркою нечітку реґресійну мо-дель у відповідності до тестуючої вибірки. In work considered technology allows to build multivariate dependence with continuous output by combining the advantages of soft computing and regression analysis, given the opportunity, the definition of importance of input variables and their necessary interactions. However, when modeling objects with continuous output when a sufficient accuracy of the determination of a precise value of the output value is necessary, the identification of the parameters of fuzzy regression equations using the least squares method and parameters of membership functions by statistical processing of expert information is not sufficient to provide the desired accuracy. It requires configuration on the training set of a fuzzy regression model in accordance with the testing sample.

Method of tribodiagnostics of d-30KU engines/KP with the use of a microwave plasma complex: results of metrological examination and analysis of the accuracy

2020 ◽  
pp. 22-29
Author(s):  
A. Bogoyavlenskiy ◽  
A. Bokov
Keyword(s):  
Gas Turbine ◽  
Microwave Plasma ◽  
Technical Condition ◽  
The State ◽  
Turbine Engines ◽  
Metrological Examination ◽  
Gas Turbine Engines ◽  
High Frequency Plasma ◽  
Frequency Plasma ◽  
Primary Standards

The article contains the results of the metrological examination and research of the accuracy indicators of a method for diagnosing aircraft gas turbine engines of the D30KU/KP family using an ultra-high-frequency plasma complex. The results of metrological examination of a complete set of regulatory documents related to the diagnostic methodology, and an analysis of the state of metrological support are provided as well. During the metrological examination, the traceability of a measuring instrument (diagnostics) – an ultrahigh-frequency plasma complex – is evaluated based on the scintillation analyzer SAM-DT-01–2. To achieve that, local verification schemes from the state primary standards of the corresponding types of measurements were built. The implementation of measures to eliminate inconsistencies identified during metrological examination allows to reduce to an acceptable level the metrological risks of adverse situations when carrying out aviation activities in industry and air transportation. In addition, the probability of occurrence of errors of the first and second kind in the technological processes of tribodiagnostics of aviation gas turbine engines is reduced when implementing a method that has passed metrological examination in real practice. At the same time, the error in determining ratings and wear indicators provides acceptable accuracy indicators and sufficient reliability in assessing the technical condition of friction units of the D-30KP/KP2/KU/KU-154 aircraft engines.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

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