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Proceedings Paper

Experiments With Temporal Reasoning Applied To Analysis Of Telemetry Data
Author(s): W. A. Perkins; A. Austin
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Paper Abstract

Many applications of expert systems to Space Station Automation, such as monitoring, planning, and scheduling will involve reasoning about attributes of objects at different times. For example, in monitoring, the system must reason about changes in signal parameters over time because causal relationships among events are important. In order to reason efficiently and concurrently about attributes with different values at different times, different time formats, and different time validity conditions requires more complex knowledge representations than are generally available in expert systems. Representation issues dealing with point times, intervals, and relative times must also be resolved. We have implemented a temporal reasoning capability in a generic expert system shell (LES) to address these issues and to increase the flexibility of the knowledge representation for a variety of applications. For its first application, we chose monitoring of telemetry data from a satellite (the Space Telescope). Our work involved just the RCE (Rotor Controlled Electronics) bearing, a component of the reaction-wheels subsystem which has attributes such as ACTUAL-TEMPERATURE of the bearing, WHEEL-SPEED, and MOTOR-CURRENT. This task consists of collecting one attribute value per sensor per cycle, checking each value to see if it is within the acceptable range, and storing the each value with a time tag in the database. Processing becomes more complex when one or more readings are out of their acceptable range. The analysis to discover the cause involves examining several cycles of readings, as well as comparing the readings of different sensors over time. The temporal reasoning capability in LES allowed us to compare the most recent readings of two sensors; or to compare one current reading with a value collected some time earlier; or to collect several consecutive readings which are analyzed for trends. In addition, having time tags associated with attribute values permitted us to diagnose different problems occurring at different times with the same component. This would be very difficult to handle without temporal reasoning.

Paper Details

Date Published: 31 October 1987
PDF: 9 pages
Proc. SPIE 0851, Space Station Automation III, (31 October 1987); doi: 10.1117/12.942885
Show Author Affiliations
W. A. Perkins, Artificial Intelligence Center (United States)
A. Austin, Artificial Intelligence Center (United States)


Published in SPIE Proceedings Vol. 0851:
Space Station Automation III
Wun C. Chiou, Editor(s)

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