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

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.