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

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.

Authors:
 [1];  [1];  [2];  [1];  [1];  [1]
  1. Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
  2. Mechanical Engineering Department, University of Glasgow, Glasgow G12 8QQ, UK
Publication Date:
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1198501
Resource Type:
Published Article
Journal Name:
Science and Technology of Nuclear Installations
Additional Journal Information:
Journal Name: Science and Technology of Nuclear Installations Journal Volume: 2013; Journal ID: ISSN 1687-6075
Publisher:
Hindawi Publishing Corporation
Country of Publication:
Egypt
Language:
English

Citation Formats

Burr, Tom, Hamada, Michael S., Howell, John, Skurikhin, Misha, Ticknor, Larry, and Weaver, Brian. Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism. Egypt: N. p., 2013. Web. doi:10.1155/2013/705878.
Burr, Tom, Hamada, Michael S., Howell, John, Skurikhin, Misha, Ticknor, Larry, & Weaver, Brian. Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism. Egypt. doi:10.1155/2013/705878.
Burr, Tom, Hamada, Michael S., Howell, John, Skurikhin, Misha, Ticknor, Larry, and Weaver, Brian. Tue . "Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism". Egypt. doi:10.1155/2013/705878.
@article{osti_1198501,
title = {Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism},
author = {Burr, Tom and Hamada, Michael S. and Howell, John and Skurikhin, Misha and Ticknor, Larry and Weaver, Brian},
abstractNote = {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.},
doi = {10.1155/2013/705878},
journal = {Science and Technology of Nuclear Installations},
number = ,
volume = 2013,
place = {Egypt},
year = {2013},
month = {1}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1155/2013/705878

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Cited by: 1 work
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