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 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 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.

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.

Transient and Steady-State Adaptive Fueling Control of Spark Ignition IC Engines

2000 ◽  
Author(s):  
David J. Stroh ◽  
Matthew A. Franchek ◽  
James M. Kerns
Keyword(s):  
Control System ◽  
Steady State ◽  
Internal Combustion Engines ◽  
Least Squares Method ◽  
Nonlinear Models ◽  
Spark Ignition ◽  
Recursive Least Squares ◽  
Model Based ◽  
Feedforward Controller ◽  
Fueling Control

Abstract Presented in this paper is an adaptive, model based, fueling control system for spark ignition-internal combustion engines. Since the fueling control system is model based, the engine maps currently used in engine fueling control are eliminated. This proposed fueling control system is modular and can therefore accommodate changes in the engine sensor set such as replacing the mass-air flow sensor with a manifold air pressure sensor. The fueling algorithm can operate with either a switching type O2 sensor or a linear O2 sensor. The fueling control system is also parceled into steady state fueling compensation and transient fueling compensation. This feature provides the distinction between fueling control adaptation for transient fueling and steady state fueling. The steady state feedforward controller is comprised of two nonlinear models. These models are adapted via a recursive least squares method to accommodate product variability, engine aging, and changes in the operating environment. The transient fueling compensation also utilizes a feedforward controller that captures the essential dynamic characteristics of the transient fueling operation. This controller is measured using a frequency domain system identification approach. This proposed fueling control system is demonstrated on a Ford 4.6L V-8 fuel injected engine.

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 A Simulation and Experimental Study on Identification of an Electromechanical System

2022 ◽  
Vol 16 ◽  
pp. 26-33
Author(s):  
Tolgay Kara ◽  
Sawsan Abokoos
Keyword(s):  
Experimental Data ◽  
Nonlinear System ◽  
Least Squares ◽  
Position Control ◽  
Nonlinear Models ◽  
Dead Zone ◽  
Nonlinear Effects ◽  
Recursive Least Squares ◽  
Suitable Model ◽  
Linear And Nonlinear

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

scholarly journals Comparison of Two Methods Forecasting Binding Rate of Plasma Protein

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Liu Hongjiu ◽  
Hu Yanrong
Keyword(s):  
Plasma Protein ◽  
Cross Validation ◽  
Nonlinear Models ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Quantitative Structure ◽  
Binding Rate ◽  
Structure Activity ◽  
Linear And Nonlinear

By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficientsRcv2of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.

Determinants of Export Oriented Entrepreneurs from Southeast Europe Region

2018 ◽  
Vol 56 (4) ◽  
pp. 439-452
Author(s):  
Bojan Leković ◽  
Miodrag Petrović ◽  
Nemanja Berber
Keyword(s):  
Early Stage ◽  
New Technology ◽  
Geographical Area ◽  
Southeast Europe ◽  
Social Phenomena ◽  
East Europe ◽  
Individual Level ◽  
International Orientation ◽  
Entrepreneurial Behaviour ◽  
South East Europe

Abstract The subject of this research represents analysis of internationalisation activity of early-stage entrepreneurs in South East Europe region (SEE). The goal of this study is to determine characteristics of export oriented entrepreneurs from SEE region through the study of factors influencing international orientation. Geographical area of this research consists of countries from the South East Europe region. The research sample was formed on the basis of GEM - Global Individual Level Data, covering six countries (Slovenia, Croatia, Hungary, Romania, Bosnia and Herzegovina, and FYR of Macedonia), 12027 respondents of which 645 export-oriented entrepreneurs were identified. Collected data are processed using the software package for statistical analysis. With the help of Ordinal Logistic regression, significant influence of individual factors on the international orientation of entrepreneurs was identified. Research results showed that entrepreneurial motives, entrepreneurial KSA’s, innovation and new technology have a positive relationship with internationalization activity. One of the significant limitations of this paper is the lower Nagelkerk coefficient, which is characteristic for social phenomena. Bearing in mind the fact that this research trying to explain entrepreneurial behaviour, lower coefficients can be very meaningfull.

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.

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