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Proceedings Paper

Real-Time Knowledge-Based Monitoring Of Telemetry Data
Author(s): Jackson Y. Read; James L. Schmidt; Simon M. Kao; Thomas J. Laffey
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Paper Abstract

This paper describes a multiprocessing architecture environment. for performing real-time monitoring and analysis using knowledge-based problem solving techniques. To handle asynchronous inputs and perform in real time, the system consists of three or more separate processes which run concurrently on one or more processors and communicate via a message passing scheme. The Data Management. Process gathers, compresses, scales and sends the incoming telemetry data to other tasks. The Inference Process consists of a proprietary high performance inference engine that runs at 1000 rules per second using telemetry data to perform a real-time analysis on the state and health of the Space Telescope. The I/O Process receives telemetry monitors from the Data Management Process and status messages from the Inference Process, updates its graphical displays in real time, and acts as the interface to the console operator. The operator sees a hierarchy or displays (some of them schematics) which can be traversed using a mouse. The user can display real-time graphs of the telemetry monitors. The multiprocessing architecture has been interfaced to a simulator and is able to process the incoming telemetry in "real-time" (i.e., several hundred telemetry monitors per second.).

Paper Details

Date Published: 29 March 1988
PDF: 8 pages
Proc. SPIE 0937, Applications of Artificial Intelligence VI, (29 March 1988); doi: 10.1117/12.947028
Show Author Affiliations
Jackson Y. Read, Lockheed Artificial Intelligence Center (United States)
James L. Schmidt, Lockheed Artificial Intelligence Center (United States)
Simon M. Kao, Lockheed Artificial Intelligence Center (United States)
Thomas J. Laffey, Lockheed Artificial Intelligence Center (United States)


Published in SPIE Proceedings Vol. 0937:
Applications of Artificial Intelligence VI
Mohan M. Trivedi, Editor(s)

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