A Gaussian Process Regression Approach to Model Aircraft Engine Fuel Flow Rate

The problem of building statistical models of cyber-physical systems using operational data is addressed in this paper, using thecase study of aircraft engines. These models serve as a complementto physics-based models, which may not accurately reflect the operational performance of systems. The accurate modeling of fuelflow rate is an essential aspect of analyzing aircraft engine performance. In this paper, operational data from Flight Data Recordersare used to model the fuel flow rate. The independent variablesare restricted to those which are obtainable from trajectory data.Treating the engine as a statistical system, an algorithm based onGaussian Process Regression (GPR) is developed to estimate thefuel flow rate during the airborne phases of flight. The algorithmpropagates the uncertainty in the estimates in order to determineprediction intervals. The proposed GPR models are evaluated fortheir predictive performance on an independent set of flights. Theresulting estimates are also compared with those given by the Baseof Aircraft Data (BADA) model, which is widely used in aircraftperformance studies. The GPR models are shown to perform statistically significantly better than the BADA model. The GPR modelsalso provide interval estimates for the fuel flow rate which reflectthe variability seen in the data, presenting a promising approachfor data-driven modeling of cyber-physical systems.