Signal estimation and change detection in tank data for nuclear safeguards

Process monitoring (PM) is increasingly important in nuclear safeguards as a complement to mass-balance based nuclear materials accounting (NMA). Typically, PM involves more frequent but lower quality measurements than NMA. While NMA estimates special nuclear material (SNM) mass balances and uncertainties, PM often tracks SNM attributes qualitatively or in the case of solution monitoring (SM) tracks bulk mass and volume.Automatic event marking is used in several nuclear safeguards PM systems. The aims are to locate the start and stop times and signal changes associated with key events. This paper describes results using both real and simulated SM data to quantify the errors associated with imperfect marking of start and stop times of tank events such as receipts and shipments. In the context of safeguards, one can look both forward and backward in modest time intervals to recognize events. Event marking methods evaluated include differencing, multi-scale principal component analysis using wavelets, and piecewise linear regression (PLR). All methods are evaluated on both raw and smoothed data, and several smoothing options are compared, including standard filters, hybrid filters, and local kernel smoothing.The main finding for real and simulated examples considered is that a two-step strategy is most effective. First, any reasonably effective initial smoother is used to provide a good initial guess at change point locations. Second, PLR is applied, looking for one change point at a time. In contrast, PLR that allows for multiple change points simultaneously has worse performance.