Machine Learning for Rocket Propulsion Health Monitoring NASA Ames Research Center

Format:

Conference/Event

Author/Creator:

Schwabacher, Mark, author.

Conference Name:

Aerospace Technology Conference & Exposition (2005-10-03 : Grapevine, Texas, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2005

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

Notes:

Vendor supplied data

Publisher Number:

2005-01-3370

Access Restriction:

Restricted for use by site license