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...

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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
Description
Summary: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.

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