TY - JOUR
T1 - Explainable artificial intelligence for exhaust gas temperature of turbofan engines
AU - Kefalas, Marios
AU - de Santiago Rojo Jr., Juan
AU - Apostolidis, A.
AU - Van Den Herik, Dirk
AU - van Stein, Bas
AU - Back, Thomas
N1 - Funding Information:
This work is part of the research program Smart Industry SI2016 with project name CIMPLO and project number 15465, which is partly financed by the Netherlands Organisation for Scientific Research (NWO).
Publisher Copyright:
© 2022, AIAA International. All rights reserved.
PY - 2022/6
Y1 - 2022/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85131150041&partnerID=8YFLogxK
U2 - 10.2514/1.I011058
DO - 10.2514/1.I011058
M3 - Article
VL - 19
SP - 447
EP - 454
JO - Journal of Aerospace Information Systems
JF - Journal of Aerospace Information Systems
IS - 6
ER -