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A Multivariate Statistical Pattern Recognition System for Reactor Noise Analysis

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4 Author(s)
Gonzalez, R.C. ; Oak Ridge National Laboratory Oak Ridge, Tennessee 37830 ; Howington, L.C. ; Sides, W.H. ; Kryter, R.C.

A multivariate statistical pattern recognition system for reactor noise analysis was developed. 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, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1% of the mean value in selected frequency ranges were detected by the system.

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Nuclear Science, IEEE Transactions on  (Volume:23 ,  Issue: 1 )