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...
Main Authors: | , |
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Format: | Text |
Language: | English |
Published: |
2007
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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 |
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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