Title :
Sensor signal analysis by neural networks for surveillance in nuclear reactors
Author :
Keyvan, Shahla ; Rabelo, Luis C.
Author_Institution :
Dept. of Nucl. Eng., Missouri Univ., Rolla, MO, USA
fDate :
4/1/1992 12:00:00 AM
Abstract :
The application of neural networks as a tool for reactor diagnosis is examined. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of the degradation of a pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for monitoring and surveillance in nuclear reactors. The Adaptive Resonance Theory (ART 2 and ART 2A) paradigm of neural networks is used. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques and is capable of distinguishing these signals and providing a measure of the progress of the degradation. Results are presented of the analysis of these data, and the performances of ART 2-A and ART 2 for reactor signal analysis are evaluated
Keywords :
computerised monitoring; computerised signal processing; fission reactor safety; neural nets; nuclear engineering computing; ART 2; ART 2A; Adaptive Resonance Theory; neural networks; noise analysis; nuclear reactors; pump shaft degradation; pump signals; reactor diagnosis; sensor signal analysis; surveillance; wear-out monitoring system; Degradation; Inductors; Monitoring; Neural networks; Resonance; Shafts; Signal analysis; Subspace constraints; Surveillance; System testing;
Journal_Title :
Nuclear Science, IEEE Transactions on