On-line reactor monitoring with neural network for RSG-GAS

Abstract

The ANNOMA (artificial neural network of monitoring aids) system is applied to the condition monitoring and signal validation of multi purpose reactor (RSG-GAS) in Indonesia. The feedforward neural network in auto-associative mode learns reactor's normal operational data, and models the reactor dynamics during the initial learning. The basic principle of the anomaly detection is to monitor the deviation between the process signals measured from the actual reactor and the corresponding values predicted by the reactor model, i.e., the neural networks. The pattern of the deviation at each signal is utilized for the identification of anomaly, e.g. sensor failure or system fault. The on-line test results showed that the neural network successfully monitored the reactor status during power increasing and steady state operation in real-time