This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.


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    Titel :

    Machine Learning for Rocket Propulsion Health Monitoring


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:

    Kongress:

    Aerospace Technology Conference and Exposition ; 2005



    Erscheinungsdatum :

    2005-10-03




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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