GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
This provides a domain-specific tool for spacecraft mission specialists and operators to improve monitoring and operations planning, but it is incremental as it applies existing methods to new data.
The authors tackled the problem of analyzing spacecraft telemetry data by developing GalaxAI, a toolbox that uses machine learning for tasks like classification and regression, and demonstrated its utility in two spacecraft use-cases, such as predicting thermal power consumption and Van Allen belt crossings.
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.