Nuclear power plant fault-diagnosis using artificial neural networks
Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant`s training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses.
- Research Organization:
- Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Mechanical Engineering
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG02-92ER75700
- OSTI ID:
- 10140171
- Report Number(s):
- CONF-921185-2; ON: DE93010314
- Resource Relation:
- Conference: 1992 artificial neural networks in engineering conference,St. Louis, MO (United States),15-18 Nov 1992; Other Information: PBD: [1992]
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
FAULT TREE ANALYSIS
NEURAL NETWORKS
NUCLEAR POWER PLANTS
ARTIFICIAL INTELLIGENCE
REACTOR SAFETY
DIAGNOSTIC TECHNIQUES
220100
220900
990200
THEORY AND CALCULATION
MATHEMATICS AND COMPUTERS