Development of nuclear power plant diagnosis technique using neural networks

Abstract

The authors have developed a nuclear power plant diagnosis technique, transient phenomena analysis that uses neural networks. Neural networks identify failed equipment by recognizing the pattern of main plant parameters. It is possible to obtain the cause of an abnormality when a nuclear power plant is in a transient state. The neural network has 49 units on its input layer, 20 units on its hidden layer and 100 units on its output layer. Testing of the neural network was carried out with patterns that have been accumulated from past incident data by a backpropagation procedure.<>