We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, regression and structured output prediction, capable of handling high-throughput heterogeneous data. These methods allow for the construction of robust and accurate predictive models, that are in turn applied to different tasks of spacecraft monitoring and operations planning. More importantly, besides the accurate building of models, GalaxAI implements a visualisation layer, providing mission specialists and operators with a full, detailed and interpretable view of the data analysis process. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft: i) analysis and planning of Mars Express thermal power consumption and ii) predicting of INTEGRAL’s crossings through Van Allen belts.
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
2021-07-01
2121652 byte
Conference paper
Electronic Resource
English
Machine Learning Methods for Spacecraft Telemetry Mining
IEEE | 2019
|Principles of Spacecraft Telemetry Data Management
Tema Archive | 1973
|Spacecraft telemetry and command
NTRS | 1968