Abstract
The Flight Control Team Multi-Agent System (FCTMAS) study, funded by the European Space Agency (ESA), has investigated the use of multiagent systems in supporting flight control teams in routine operations. One of the scientific challenges of the FCTMAS study has been the detection of anomalies relative to a space system only on the basis of identified deviations from the nominal trends of single measurable variables. In this paper, we discuss how we addressed this challenge by looking for the best structure that aggregates a given set of models, each one returning the anomaly probability of a single measurable variable, under the assumption that there is no a priori knowledge about the structure of the space system nor about the relationships between the variables. Experiments are conducted on data of the Cryosat-2 satellite and their results are eventually summarized as a set of guidelines.
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Acknowledgment
The authors kindly acknowledge the contributions of Matteo Gallo and Matteo Garza to the development of the MCS Subsystem described in this paper.
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Amigoni, F., Ferrari Dacrema, M., Donati, A., Laroque, C., Lavagna, M., Riva, A. (2019). Aggregating Models for Anomaly Detection in Space Systems: Results from the FCTMAS Study. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_12
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DOI: https://doi.org/10.1007/978-3-030-01370-7_12
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