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Title: Neural networks and their application to nuclear power plant diagnosis

The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed.
Authors:
 [1]
  1. Argonne National Lab., IL (United States). Reactor Analysis Div.
Publication Date:
OSTI Identifier:
537352
Report Number(s):
ANL/RA/CP--91101; CONF-970815--2
ON: DE97008375; TRN: 98:000930
DOE Contract Number:
W-31109-ENG-38
Resource Type:
Conference
Resource Relation:
Conference: 11. Brazilian meeting on reactor physics and thermal hydraulics and 4th Brazilian meeting on nuclear applications, Pocos de Caldas Springs (Brazil), 18-22 Aug 1997; Other Information: PBD: [1997]
Research Org:
Argonne National Lab., IL (United States)
Sponsoring Org:
USDOE Assistant Secretary for Nuclear Energy, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
22 NUCLEAR REACTOR TECHNOLOGY; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; NEURAL NETWORKS; REACTOR MONITORING SYSTEMS; NUCLEAR POWER PLANTS; REACTOR OPERATORS; DECISION MAKING; DIAGNOSTIC TECHNIQUES; FAILURES