Neural network recognition of nuclear power plant transients
The objective of this report is to describe results obtained during the first year of funding that will lead to the development of an artificial neural network (ANN) fault - diagnostic system for the real - time classification of operational transients at nuclear power plants. The ultimate goal of this three-year project is to design, build, and test a prototype diagnostic adviser for use in the control room or technical support center at Duane Arnold Energy Center (DAEC); such a prototype could be integrated into the plant process computer or safety - parameter display system. The adviser could then warn and inform plant operators and engineers of plant component failures in a timely manner. This report describes the work accomplished in the first of three scheduled years for the project. Included herein is a summary of the first year's results as, well as individual descriptions of each of the major topics undertaken by the researchers. Also included are reprints of the articles written under this funding as well as those that were published during the funded period.
- Research Organization:
- Ames Lab., Ames, IA (United States)
- Sponsoring Organization:
- USDOE; USDOE, Washington, DC (United States)
- DOE Contract Number:
- FG02-92ER75700
- OSTI ID:
- 6930362
- Report Number(s):
- DOE/ER/75700-1; ON: DE93009774
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
NUCLEAR POWER PLANTS
REACTOR OPERATION
TRANSIENTS
DIAGNOSTIC TECHNIQUES
NEURAL NETWORKS
PROGRESS REPORT
REAL TIME SYSTEMS
DOCUMENT TYPES
NUCLEAR FACILITIES
OPERATION
POWER PLANTS
THERMAL POWER PLANTS
210000* - Nuclear Power Plants
990200 - Mathematics & Computers