Nuclear power plant fault-diagnosis using artificial neural networks

Conference · Tue Dec 31 23:00:00 EST 1991

OSTI ID:6640105

Kim, Keehoon; Aljundi, T L; Bartlett, E B

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: DOE; USDOE, Washington, DC (United States)
• DOE Contract Number: FG02-92ER75700
• OSTI ID: 6640105
• Report Number(s): CONF-921185-2; ON: DE93010314
• Country of Publication: United States
• Language: English

Related Subjects

22 GENERAL STUDIES OF NUCLEAR REACTORS
220100* -- Nuclear Reactor Technology-- Theory & Calculation
220900 -- Nuclear Reactor Technology-- Reactor Safety
99 GENERAL AND MISCELLANEOUS
990200 -- Mathematics & Computers
ARTIFICIAL INTELLIGENCE
DIAGNOSTIC TECHNIQUES
FAULT TREE ANALYSIS
NEURAL NETWORKS
NUCLEAR FACILITIES
NUCLEAR POWER PLANTS
POWER PLANTS
REACTOR SAFETY
SAFETY
SYSTEM FAILURE ANALYSIS
SYSTEMS ANALYSIS
THERMAL POWER PLANTS