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

Abstract 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 .