A REAL-TIME MONITORING-SYSTEM OF CORE SUPPORT BARREL VIBRATION USING FEM DATA AND SELF-ORGANIZING NEURAL NETWORKS

In this work a core internal vibration monitoring system which is part icularly concerned with the core support barrel (CSB) in ULJIN nuclear power plant unit 1 in Korea is developed. Flow induced vibration and aging processes in the reactor internals cause unsoundness of the inte rnal structure. In particular, the loose-joined flange between the top of the CSB and the head of the vessel may result in core or fuel dama ge accidents. In order to improve the accuracy of the conventional CSB monitoring system, signals from the piezoelectric accelerometers are used in this work instead of those from the ex-core neutron detectors. This work consists of three parts: one is the development of a suitab le tool for detecting the hold down spring broken accident or wearing out of the CSB using the Fuzzy ARTMAP (self-organizing neural network) technique; another is the generation of vibration signals to represen t the abnormal states of the CSB by finite element method (FEM) analys is and mock-up experiments; the third is the development of a graphica l man-machine interface for the practical use of the monitoring system .