Unsupervised Anomaly Detection for Liquid-Fueled Rocket

This paper describes the initial results of applying four machine-learning-based unsupervised anomaly detection algorithms—Orca, GritBot, the Inductive Monitoring System, and one-class Support Vector Machines—to historical data from the Space Shuttle Main Engine. The paper describes five anomalies d...

Full description

Bibliographic Details
Main Authors: Mark Schwabacher, Nikunj Oza
Other Authors: The Pennsylvania State University CiteSeerX Archives
Format: Text
Language:English
Published: 2007
Subjects:
Orca
Online Access:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3607
http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf
id ftciteseerx:oai:CiteSeerX.psu:10.1.1.97.3607
record_format openpolar
spelling ftciteseerx:oai:CiteSeerX.psu:10.1.1.97.3607 2023-05-15T17:53:26+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=10.1.1.97.3607 http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf en eng http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3607 http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf Metadata may be used without restrictions as long as the oai identifier remains attached to it. http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf text 2007 ftciteseerx 2016-01-08T20:03:55Z This paper describes the initial results of applying four machine-learning-based unsupervised anomaly detection algorithms—Orca, GritBot, the Inductive Monitoring System, and one-class Support Vector Machines—to historical data from the Space Shuttle Main Engine. The paper describes five anomalies detected by the four algorithms. I. Text Orca Unknown
institution Open Polar
collection Unknown
op_collection_id ftciteseerx
language English
description This paper describes the initial results of applying four machine-learning-based unsupervised anomaly detection algorithms—Orca, GritBot, the Inductive Monitoring System, and one-class Support Vector Machines—to historical data from the Space Shuttle Main Engine. The paper describes five anomalies detected by the four algorithms. I.
author2 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=10.1.1.97.3607
http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf
genre Orca
genre_facet Orca
op_source http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf
op_relation http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.97.3607
http://ase.arc.nasa.gov/people/oza/publications/files/scoz07.pdf
op_rights Metadata may be used without restrictions as long as the oai identifier remains attached to it.
_version_ 1766161152557973504

Similar Items