System for unattended surveillance of nuclear reactor behavior

A multivariate statistical pattern recognition system for reactor noise analysis is presented. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, updating, and dimensionality reduction capabilities. System design emphasizes control of the false alarm rate. Its abilities to learn normal patterns and to recognize deviations from these patterns were evaluated by experiments at the ORNL high-flux isotope reactor. Power perturbations of less than 0.1% of the mean value in selected frequency ranges were readily detected by the pattern recognition system.