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Proceedings Paper

On-Line Model-Based System For Nuclear Plant Monitoring
Author(s): L. Tsoukalas; G. W. Lee; M. Ragheb; T. McDonough; F. Nizioleki; M. Parker
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Paper Abstract

A prototypical on-line model-based system, LASALLE1, developed at the University of Illinois in collaboration with the Illinois Department of Nuclear Safety (IDNS) is described. Its main purpose is to interpret about 300 signals, updated every two minutes at IDNS from the LaSalle Nuclear Power Plant, and to diagnose possible abnormal conditions. It is written in VAX/VMS OPS5 and operates on both the on-line and testing modes. In its knowledge base, operator and plant actions pertaining to the Emergency Operating Procedure(EOP) A-01, are encoded. This is a procedure driven by a reactor's coolant level and pressure signals; with the purpose of shutting down the reactor, maintaining adequate core cooling and reducing the reactor pressure and temperature to cold shutdown conditions ( about 90 to 200 °F). The monitoring of the procedure is performed from the perspective of Emergency Preparedness. Two major issues are addressed in this system. First, the management of the short-term or working memory of the system. LASALLE1 must reach its inferences, display its conclusion and update a message file every two minutes before a new set of data arrives from the plant. This was achieved by superimposing additional layers of control over the inferencing strategies inherent in OPS5, and developing special rules for the management of the used or outdated information. The second issue is the representation of information and its uncertainty. The concepts of information granularity and performance-level, which are based on a coupling of Probability Theory and the theory of Fuzzy Sets, are used for this purpose. The estimation of the performance-level incorporates a mathematical methodology which accounts for two types of uncertainty encountered in monitoring physical systems: Random uncertainty, in the form of of probability density functions generated by observations, measurements and sensors data and fuzzy uncertainty represented by membership functions based on symbolic , stochastic or numerical models estimating the "plausible", "possible" or "expected" values of the system parameters. Examples from both the on-line mode and the testing mode of the system will be discussed to illustrate the present methodology.

Paper Details

Date Published: 21 March 1989
PDF: 10 pages
Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969287
Show Author Affiliations
L. Tsoukalas, University of Illinois (United States)
G. W. Lee, University of Illinois (United States)
M. Ragheb, University of Illinois (United States)
T. McDonough, Illinois Department of Nuclear (United States)
F. Nizioleki, Illinois Department of Nuclear (United States)
M. Parker, Illinois Department of Nuclear (United States)

Published in SPIE Proceedings Vol. 1095:
Applications of Artificial Intelligence VII
Mohan M. Trivedi, Editor(s)

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