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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data




    Publication date :

    2021-07-01


    Size :

    2121652 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Machine Learning Methods for Spacecraft Telemetry Mining

    Ibrahim, Sara K. / Ahmed, Ayman / Zeidan, M. Amal Eldin et al. | IEEE | 2019




    HOUSEKEEPING TELEMETRY ANALYSIS FOR SPACECRAFT HEALTH MONITORING AND PREDICTIVE DIAGNOSIS USING MACHINE LEARNING

    Mukhachev, Petr / Sadretdinov, Tagir / Ivanov, Anton et al. | TIBKAT | 2021