Aviation Academy

Explainable artificial intelligence for exhaust gas temperature of turbofan engines


Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure.

In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.

Reference Kefalas, M., de Santiago Rojo Jr., J., Apostolidis, A., Van Den Herik, D., van Stein, B., & Back, T. (2022). Explainable artificial intelligence for exhaust gas temperature of turbofan engines. Journal of Aerospace Information Systems, 19(6), 447-454. https://doi.org/10.2514/1.I011058
1 June 2022

Publication date

Jun 2022


Marios Kefalas
Juan de Santiago Rojo Jr.
A. Apostolidis
Dirk Van Den Herik
Bas van Stein
Thomas Back


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