2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International

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 ex...

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Bibliographic Details
Main Author: Mark Schwabacher
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Orca
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.4065
http://www.ic.arc.nasa.gov/m/pub/1032h/1032%20(Schwabacher).pdf
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Summary: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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