A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

Fentaye, Amare Desalegn; Baheta, Aklilu Tesfamichael; Gilani, Syed I.U.; Kyprianidis, Konstantinos

doi:10.3390/aerospace6070083

Cited by 101 publications

(68 citation statements)

References 169 publications

(250 reference statements)

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“…Conventionally, maximum value of 0.05 to 0.07 (5 to 7 %) is considered for the fault parameters (Fentaye et al, 2019). For this reason, a total range of the variation was approximately limited by 0.1 for both engines.…”

Section: Optimal Variation Value Of Fault Parametersmentioning

confidence: 99%

“…Their main sources are: Fouling, corrosion, erosion, worn seals, object damage and increased blade tip clearance. The information on these faults and their consequences can be found in (Fentaye et al, 2019 andTahan et al, 2017). Fault manifestations are rare in real engines and therefore their mathematical models are involved in fault description.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of nonlinear gas turbine models using influence coefficients

González-Castillo

Loboda

2021

iit

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The limited availability of gas turbine data, especially faults data and the high costs and risks of using test benches to obtain it,causes that rarely have enough data for form a fault classification. These circumstances have created the need to develop models that can provide simulated data. The quality of the data generated depends on the complexity of the thermodynamic model and the mathematical solution. A method to evaluate the accuracy of the models and their linearization capacity is presented. The method is applied to the models of a turbo shaft and a turbo fan of the commercial software GasTurb 12, as an example. It was simulated a wide database with influence of fault parameters and condition operation, then it calculed the influence matrix ""H"" and ""G"" for prove the influence theirs on behavior of the models. The results show that if the model is sufficiently accuracy, it is possible to find an adequate interval where the linearization errors are not very large and it is just possible the linearization.

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Section: Optimal Variation Value Of Fault Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of nonlinear gas turbine models using influence coefficients

González-Castillo

Loboda

2021

iit

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“…The ability to implement prior knowledge in different forms to stabilize the solution is the main contribution of the paper. Regularized multi-criteria identification enables engine users to implement Non-Linear GPA [1,2] in ill-conditioned configurations with a limited number of measured parameters.…”

Section: Basic Identification Proceduresmentioning

confidence: 99%

“…Nowadays, different approaches are used for processing flight and test-cell data to estimate component performance and detect faults [1]. These methods can be divided into two groups [2]:…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

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“…This paper considers the solution based on the genetic algorithm [31], which is adapted to specifics of the engine model matching [32]. The proposed approach enables engine users to implement the Gas Path Analysis at the limited number of measured parameters [33,34].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Khustochka¹,

Yepifanov²,

Zelenskyi³

et al. 2019

Preprint

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Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

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A Review on Gas Turbine Gas-Path Diagnostics: State-of-the-Art Methods, Challenges and Opportunities

Cited by 101 publications

References 169 publications

Analysis of nonlinear gas turbine models using influence coefficients

Analysis of nonlinear gas turbine models using influence coefficients

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

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