Neural networks and their application to nuclear power plant diagnosis

We present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal=hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for NPP operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-base diagnostic systems are identified. A total of thirty-four publications are reviewed. (author). 35 refs