The added value of Advanced Feature Engineering and Selection for Machine Learning models for Spacecraft behavior prediction

Ying Gu, Gagan Manjunatha Gowda, Praveenkumar Jayanna, Redouance Boumghar, Lule Lucas, Ansgar Bernardi, Andreas Dengel

In: 15. International Conference on Space Operations. AIAA SPACE Conference and Exposition (SPACE-2018) May 28-June 1 Marseille France ScholarOne Abstracts 6/2018.


This paper describes the approach of one of the top ranked prediction models at the Mars Express Power Challenge. Advanced feature engineering methods, and information mining from the Mars Express Orbiter open data constitute an important step during which domain knowledge is incorporated. The available data describes the thermal subsystem power consumption and the operational context of the Mars Express Orbiter. The power produced by the solar panels and the one consumed by the orbiter’s platform are well known by operators, as opposed to the power consumption of the thermal subsystem which reacts to keep subsystems at a given range of working temperatures. The residual power is then used for scientific observation. This paper presents an iterative and interactive pipeline framework which uses machine learning to predict, with more accuracy, the thermal power consumption. The prediction model, along with the estimation of the thermal power consumption, also provides insight on the effect of the context which could help operators to exploit spacecraft resources, thereby prolonged mission life.


German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz