Spacecraft housekeeping telemetry is monitored at flight control centers by the operations engineers using tools that can perform limit checking or simple trend analysis. Recent developments in machine learning techniques for anomaly detection enables the implementation of more sophisticated systems that aim to augment current state-of-theart mission tools to provide valuable decision support for the spacecraft operators, assisting in anomaly detection and potentially saving console time for the engineers. We will show some results of the implementation of an anomaly detection tool for the NASA Mars Science Laboratory mission.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen


    Exportieren, teilen und zitieren



    Titel :

    Telemetry Anomaly Detection System using Machine Learning to Streamline Mission Operations


    Beteiligte:

    Erscheinungsdatum :

    27.09.2017


    Medientyp :

    Preprint


    Format :

    Keine Angabe


    Sprache :

    Englisch



    Telemetry Anomaly Detection System Using Machine Learning to Streamline Mission Operations

    Fernandez, Michela Munoz / Yue, Yisong / Weber, Romann | IEEE | 2017



    An Explainable Machine Learning Approach for Anomaly Detection in Satellite Telemetry Data

    Kricheff, Seth / Maxwell, Emily / Plaks, Connor et al. | IEEE | 2024



    Communications streamline yard operations

    Engineering Index Backfile | 1954