Extended Surrogate Modelling for Gas Turbine Diagnostics & Prognostics

Abstract

As turbofan technology advances, periodic engine inspection and maintenance still remain a significant part of aircraft operational costs. Operators are thus looking to engine condition-based maintenance (ECBM), leveraging sensor data and numerical engine models for continuous diagnostics. KLM Engine Services developed a surrogate model based on the High Dimensional Model Representation approach, capable of processing a large volume of engine data at a lower computational cost to estimate engine component condition. This study proposes enhancements to expand the model's capabilities, incorporating additional engine parameters and extending the operational envelope. The augmented surrogate model was then combined with a Long Short Term Memory network capable of predicting component condition based on trends generated from the surrogate model. The framework proposed has demonstrated the potential advantages of combining the surrogate and prediction model for engine diagnostics and prognostics, serving as a valuable starting point for future ECBM projects at KLM ES.