Application of Markov chains to identification of gas turbine engine dynamic models
The paper addresses the practical problems of dynamic modelling of aero gas turbine engines for condition-monitoring purposes. The Markov chain technique is implemented to perform identification of the engine dynamic models using the engine normal flight data. This includes identifiability analysis and model estimation. When identifying the model, experimental data should be sufficiently informative for identification. A possible technique for identifiability analysis is proposed on the basis of non-parametric models in the form of controllable Markov chains. At the stage of the model estimation, Markov chains are introduced to provide more functionality and versatility for dynamic modelling of gas turbines.
identification dynamic modelling real-time simulation