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Detecting faults in a nuclear power plant by using dynamic node architecture artificial neural networks

Journal Article · · Nuclear Science and Engineering; (United States)
OSTI ID:7201237
;  [1]
  1. Iowa State Univ., Ames, IA (United States). Dept. of Mechanical Engineering
+ Show Author Affiliations

An artificial neural network (ANN)-based diagnostic adviser capable of identifying the operating status of a nuclear power plant is described. A dynamic node architecture scheme is used to optimize the architectures of the two backpropagation ANNs that embody the advisor. The first or root network is used to determine whether or not the plant is in a normal operating condition. If the plant is not in a normal condition, the second or classifier network is used to recognize the particular off-normal condition or transient taking place. These networks are developed using simulated plant behavior during both normal and abnormal conditions. The adviser is effective at diagnosing 27 distinct transients based on 43 scenarios simulated at various severities that contain up to 3% noise.

DOE Contract Number:
FG02-92ER75700
OSTI ID:
7201237
Journal Information:
Nuclear Science and Engineering; (United States), Journal Name: Nuclear Science and Engineering; (United States) Vol. 116:4; ISSN NSENAO; ISSN 0029-5639
Country of Publication:
United States
Language:
English

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Related Subjects

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS
210100 -- Power Reactors
Nonbreeding
Light-Water Moderated
Boiling Water Cooled
22 GENERAL STUDIES OF NUCLEAR REACTORS
220400 -- Nuclear Reactor Technology-- Control Systems
220900* -- Nuclear Reactor Technology-- Reactor Safety
BWR TYPE REACTORS
DETECTION
DIAGNOSTIC TECHNIQUES
ENRICHED URANIUM REACTORS
FAILURES
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
OPERATION
POWER PLANTS
POWER REACTORS
REACTOR MONITORING SYSTEMS
REACTOR OPERATION
REACTOR SAFETY
REACTORS
SAFETY
THERMAL POWER PLANTS
THERMAL REACTORS
TRANSIENTS
WATER COOLED REACTORS
WATER MODERATED REACTORS