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Accession Number ADA562487
Title Data-Driven Anomaly Detection Performance for the Ares I-X Ground Diagnostic Prototype.
Publication Date Oct 2010
Media Count 14p
Personal Author B. L. Matthews M. A. Schwabacher R. A. Martin
Abstract In this paper, we will assess the performance of a data-driven anomaly detection algorithm, the Inductive Monitoring System (IMS), which can be used to detect simulated Thrust Vector Control (TVC) system failures. However, the ability of IMS to detect these failures in a true operational setting may be related to the realistic nature of how they are simulated. As such, we will investigate both a low fidelity and high fidelity approach to simulating such failures, with the latter based upon the underlying physics. Furthermore, the ability of IMS to detect anomalies that were previously unknown and not previously simulated will be studied in earnest, as well as apparent deficiencies or misapplications that result from using the data-driven paradigm. Our conclusions indicate that robust detection performance of simulated failures using IMS is not appreciably affected by the use of a high fidelity simulation. However, we have found that the inclusion of a data-driven algorithm such as IMS into a suite of deployable health management technologies does add significant value.
Keywords Algorithms
Data-driven anomaly detection
Diagnostic equipment
Software tools
Thrust vector control systems

Source Agency Non Paid ADAS
NTIS Subject Category 62B - Computer Software
Corporate Author National Aeronautics and Space Administration, Moffett Field, CA. Ames Research Center.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1225
Contract Number N/A

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