Publikation
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.
Zusammenfassung
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.