APPLICATION OF NEURAL NETWORKS TO MULTIPLE ALARM PROCESSING AND DIAGNOSIS IN NUCLEAR-POWER-PLANTS

Abstract

We present the feasibility study of multiple alarm processing and diagnosis using neural networks. The back-propagation network (BPN) algorithm is applied to the training of multiple alarm patterns for the identification of faults in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. A number of case studies are performed with emphasis on the applicability of the neural network to the pattern recognition of multiple alarms. Based on the case studies, the neural network can identify the cause of multiple alarms well, although untrained, incomplete/sensor-failed or time-varying alarm symptoms are given. Also, multiple faults are easily identified with a given alarm pattern.