Surrogate Model Based Diagnostics for the GEnx-1B Turbofan Engine

Abstract

Over the years, aero engines have evolved into the efficient turbofans present on commercial airliners today. Although these engines are very reliable, they still experience degradations in performance and reductions in component health. The degradation affects the operation of the engine, which can be measured using the pressure, temperature, and rotational speed sensors. Using accurate engine models together with engine sensor measurements, the condition of individual components can be determined. Surrogate models of a non-linear Gas Path Analysis model have been developed for the GEnx-1B turbofan in the form of High Dimensional Model Representations. It is determined that the surrogate models developed are able to determine component conditions with a high accuracy and low computational complexity. The models are able to properly identify the effects of water-washes and turbine failures when applied to real-world on-wing data. Due to the low computational complexity of the models, they provide the possibility for fleet-wide continuous engine diagnostics.