The AutoAssociative Neural Network in signal analysis: II. Application to on-line monitoring of a simulated BWR component

In this paper, Robust AutoAssociative Neural Networks (RAANN) are applied to a series of signals produced by the Halden simulator of the 1200 MWe BWR Forsmark-3 plant in Sweden. The applications concern: • correction of drifts and gross errors in sensors, for diagnostic and control purposes, • cluster analysis, to individuate a failed component and the intensity of the failure, • forecasting system signals, for safety or economic purposes, • reconstruction of unmeasured signals (virtual sensors). In the attainment of the above results, the geometric interpretation of the mapping performed by the network, propounded in Part I of this work, has provided a reasoned choice of the most critical free parameter, i.e., the number f of nodes of the bottleneck layer, thus allowing a deep understanding of the network functioning and also avoiding the traditional and troubling procedure of selection by trial-and-error. The theoretical basis of this analysis, discussed in details in the companion paper, is founded on the idea of dimension and in particular of fractal dimension, which has been used as a numerical estimator of f.