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Inductive System Health MonitoringThe Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficult to model (simulate) with a computer or which require computer models that are too complex to use for real time monitoring. IMS uses nominal data sets collected either directly from the system or from simulations to build a knowledge base that can be used to detect anomalous behavior in the system. Machine learning and data mining techniques are used to characterize typical system behavior by extracting general classes of nominal data from archived data sets. IMS is able to monitor the system by comparing real time operational data with these classes. We present a description of learning and monitoring method used by IMS and summarize some recent IMS results.
Document ID
Document Type
Preprint (Draft being sent to journal)
Iverson, David L. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
September 7, 2013
Publication Date
January 1, 2004
Subject Category
Documentation and Information Science
Meeting Information
International Conference on Artificial Intelligence(Las Vegas, NV)
Distribution Limits
Work of the US Gov. Public Use Permitted.

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NameType 20040068062.pdf STI