Nuclear reactor monitoring with the combination of neural network and expert system

Nabeshima, Kunihiko  ; Suzudo, Tomoaki   ; Ono, Tomio*; Kudo, Kazuhiko*

This study presents a hybrid monitoring system for nuclear reactor utilizing neural networks and a rule-based real-time expert system. The whole monitoring system including a data acquisition system and the advisory displays has been tested by an on-line simulator of pressurized water reactor. From the testing results, it was shown that the neural network in the monitoring system successfully modeled the plant dynamics and detected the symptoms of anomalies earlier than the conventional alarm system. The real-time expert system also worked satisfactorily in diagnosing and displaying the system status by using the outputs of neural networks and a priori knowledge base.