Unsupervised Anomaly Detection for Liquid-Fueled Rocket
This article describes the results of applying four unsupervised anomaly detection algorithms 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...
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ftciteseerx:oai:CiteSeerX.psu:10.1.1.157.7461 2023-05-15T17:53:49+02:00 Unsupervised Anomaly Detection for Liquid-Fueled Rocket Mark Schwabacher Nikunj Oza The Pennsylvania State University CiteSeerX Archives 2007 application/pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.7461 en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.7461 Metadata may be used without restrictions as long as the oai identifier remains attached to it. https://dashlink.arc.nasa.gov/static/dashlink/media/paper/AIAA-42783-102.pdf text 2007 ftciteseerx 2016-01-07T15:33:52Z This article describes the results of applying four unsupervised anomaly detection algorithms 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 article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms. I. Text Orca Unknown |
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description |
This article describes the results of applying four unsupervised anomaly detection algorithms 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 article describes nine anomalies detected by the four algorithms. The four algorithms use four different definitions of anomalousness. Orca uses a nearest-neighbor approach, defining a point to be an anomaly if its nearest neighbors in the data space are far away from it. The Inductive Monitoring System clusters the training data, and then uses the distance to the nearest cluster as its measure of anomalousness. GritBot learns rules from the training data, and then classifies points as anomalous if they violate these rules. One-class support vector machines map the data into a high-dimensional space in which most of the normal points are on one side of a hyperplane, and then classify points on the other side of the hyperplane as anomalous. Because of these different definitions of anomalousness, different algorithms detect different anomalies. We therefore conclude that it is useful to use multiple algorithms. I. |
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The Pennsylvania State University CiteSeerX Archives |
format |
Text |
author |
Mark Schwabacher Nikunj Oza |
spellingShingle |
Mark Schwabacher Nikunj Oza Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
author_facet |
Mark Schwabacher Nikunj Oza |
author_sort |
Mark Schwabacher |
title |
Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
title_short |
Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
title_full |
Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
title_fullStr |
Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
title_full_unstemmed |
Unsupervised Anomaly Detection for Liquid-Fueled Rocket |
title_sort |
unsupervised anomaly detection for liquid-fueled rocket |
publishDate |
2007 |
url |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.7461 |
genre |
Orca |
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Orca |
op_source |
https://dashlink.arc.nasa.gov/static/dashlink/media/paper/AIAA-42783-102.pdf |
op_relation |
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.7461 |
op_rights |
Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
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1766161523543113728 |