Application of Neural Networks in Reactor Diagnostics and Monitoring (original) (raw)

2000, Studies in Fuzziness and Soft Computing

This chapter gives an account of the use of neural network techniques in reactor diagnostics and monitoring through a few concrete examples of successful practical applications. Diagnostic problems require the solution of a so-called inverse task, namely to determine the normal or abnormal values of some system parameters ("noise sources ''), that cannot be directly measured, by observing their effect on other, measurable parameters ("reactor noise''). In the past, such inversion or urifolding techniques were possible to perform only if the direct task, i. e., calculation of the induced noise from the noise sources, could be made with a compact analytical solution. This condition hindered wide-spread practical applications. The use of artifiCial neural networks (ANN) presents a very powerful solution to the unfolding problem, since it does not require an analytical relationship between the cause and the reason. ANNs can be trained on simulated data. In the nuclear industry very powerful and accurate numerical methods exist to calculate process variables for operating plant, thus ANNs trained on simulated data can be used in real applications. This will be demonstrated through examples in this chapter.