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Science and Technology of Nuclear Installations
Volume 2013, Article ID 705878, 18 pages
http://dx.doi.org/10.1155/2013/705878
Research Article

Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism

1Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2Mechanical Engineering Department, University of Glasgow, Glasgow G12 8QQ, UK

Received 7 December 2012; Revised 10 May 2013; Accepted 15 May 2013

Academic Editor: Michael F. Simpson

Copyright © 2013 Tom Burr et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Process monitoring (PM) for nuclear safeguards sometimes requires estimation of thresholds corresponding to small false alarm rates. Threshold estimation dates to the 1920s with the Shewhart control chart; however, because possible new roles for PM are being evaluated in nuclear safeguards, it is timely to consider modern model selection options in the context of threshold estimation. One of the possible new PM roles involves PM residuals, where a residual is defined as residual = data − prediction. This paper reviews alarm threshold estimation, introduces model selection options, and considers a range of assumptions regarding the data-generating mechanism for PM residuals. Two PM examples from nuclear safeguards are included to motivate the need for alarm threshold estimation. The first example involves mixtures of probability distributions that arise in solution monitoring, which is a common type of PM. The second example involves periodic partial cleanout of in-process inventory, leading to challenging structure in the time series of PM residuals.