Neural Based Fault Detection and Identification for a Nuclear Reactor

Abstract In this paper, an approach based on neural networks and mathematical models for detecting and diagnosing instrument failures in the pressurized water reactor(PWR) of the H.B. Robinson nuclear plant is presented. Multilayer neural networks are used at the first level for identification of plant parameters and at the second level for distinguishing parameter variations and uncertainties from possible faults, and as a pattern recognizer in the third level for the detection of faulty instruments. The design approach was able to simultaneously classify single and multiple anomalies such as sensor and actuator failures under plant parameter uncertainties.