Ashok Srivastava

Member since: Jan 05, 2014, Verizon

Comparative Analysis of Data-Driven Anomaly Detection Methods

Shared by Ashok Srivastava, updated on Sep 22, 2010


Author(s) :
Bryan Matthews, Ashok Srivastava

This paper provides a review of three different advanced machine learning algorithms for anomaly detection in continuous data streams from a ground-test firing of a subscale Solid Rocket Motor (SRM). This study compares Orca, one-class support vector machines, and the Inductive Monitoring System (IMS) for anomaly detection on the data streams. We measure the performance of the algorithm with respect to the detection horizon for situations where fault information is available. These algorithms have been also studied by the present authors (and other co-authors) as applied to liquid propulsion systems. The trade space will be explored between these algorithms for both types of propulsion systems.

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Publication Name
JANNAF Conference on Propulsion Systems
Publication Location
Proceedings of the Joint Army Navy NASA Air Force Conference on Propulsion, Orlando, FL 2008
Year Published


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