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.
Machine Learning for Rocket Propulsion Health Monitoring
Sae Technical Papers
Aerospace Technology Conference and Exposition ; 2005
2005-10-03
Aufsatz (Konferenz)
Englisch
Machine Learning for Rocket Propuislsion Health Monitoring
British Library Conference Proceedings | 2005
|PRIMARY-STAGE PROPULSION - Liquid-Propellant Rocket Engine Health Monitoring Techniques
Online Contents | 1998
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