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...
Main Author: | |
---|---|
Other Authors: | |
Format: | Text |
Language: | English |
Subjects: | |
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 |
id |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.154.4065 |
---|---|
record_format |
openpolar |
spelling |
ftciteseerx:oai:CiteSeerX.psu:10.1.1.154.4065 2023-05-15T17:53:32+02:00 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International Mark Schwabacher The Pennsylvania State University CiteSeerX Archives application/pdf 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 en eng 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 Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://www.ic.arc.nasa.gov/m/pub/1032h/1032%20(Schwabacher).pdf text ftciteseerx 2016-01-07T15:27:34Z 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 |
collection |
Unknown |
op_collection_id |
ftciteseerx |
language |
English |
description |
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 |
author |
Mark Schwabacher |
spellingShingle |
Mark Schwabacher 2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
author_facet |
Mark Schwabacher |
author_sort |
Mark Schwabacher |
title |
2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
title_short |
2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
title_full |
2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
title_fullStr |
2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
title_full_unstemmed |
2005-01-3370 Machine Learning for Rocket Propulsion Health Monitoring Copyright © 2005 SAE International |
title_sort |
2005-01-3370 machine learning for rocket propulsion health monitoring copyright © 2005 sae international |
url |
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 |
genre |
Orca |
genre_facet |
Orca |
op_source |
http://www.ic.arc.nasa.gov/m/pub/1032h/1032%20(Schwabacher).pdf |
op_relation |
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 |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
_version_ |
1766161242775355392 |