On/Off State Classification of a Reactor Facility Using Gas Effluence Measurements
Inferring on/off operational state of a reactor facility using measurements from an independent monitoring system is critical to the assessment of its compliance to agreements. In this paper, we consider the problem of inferring on/off state of a reactor facility using measurements of Ar-41, Cs-138, and Xe-138 gas effluence types collected at the facility’s off-gas stack. We present classifiers based on thresholding measurements of individual effluence types, and then present fusers that combine their outputs or measurements. We present five fusers based on simple majority rule, Chow’s pattern recognition function, Fisher’s combined p-value statistic, physics-based Poisson radiation counts model, and correlation coefficient method. In addition, we also test five machine learning methods based on non-linear classifiers, which are available as R packages. Finally, our results show that: (i) these gas effluence measurements are effective in inferring on/off state of a reactor facility; for example, best fusers achieve ~97% detection at ~1% false alarm rate, and (ii) fusers that combine all effluence types based on physics-based models, correlation coefficient and Fisher’s method outperform simple majority rule, Chow’s fusers and machine learning methods, as well as when they are applied to individual and pairs of effluence types.