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...

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Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Subjects:
Orca
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7800
http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf
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spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.140.7800 2023-05-15T17:53:32+02:00 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring The Pennsylvania State University CiteSeerX Archives application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7800 http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7800 http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf text ftciteseerx 2016-01-07T14:56:34Z 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 experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms. Text Orca Unknown
institution Open Polar
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op_collection_id ftciteseerx
language English
description 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 experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.
author2 The Pennsylvania State University CiteSeerX Archives
format Text
title 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
spellingShingle 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
title_short 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
title_full 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
title_fullStr 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
title_full_unstemmed 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring
title_sort 2005-01-3370 machine learning for rocket propulsion health monitoring
url http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7800
http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf
genre Orca
genre_facet Orca
op_source http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7800
http://ic.arc.nasa.gov/m/profile/schwabac/2005-01-3370-revised.pdf
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
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