Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism
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 ti...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2013/705878 |
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| _version_ | 1832553663201017856 |
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| author | Tom Burr Michael S. Hamada John Howell Misha Skurikhin Larry Ticknor Brian Weaver |
| author_facet | Tom Burr Michael S. Hamada John Howell Misha Skurikhin Larry Ticknor Brian Weaver |
| author_sort | Tom Burr |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-19ea64e50e544adcbcd192b2fb014633 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-19ea64e50e544adcbcd192b2fb0146332025-02-03T05:53:32ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832013-01-01201310.1155/2013/705878705878Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating MechanismTom Burr0Michael S. Hamada1John Howell2Misha Skurikhin3Larry Ticknor4Brian Weaver5Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USAStatistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USAMechanical Engineering Department, University of Glasgow, Glasgow G12 8QQ, UKStatistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USAStatistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USAStatistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USAProcess 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.http://dx.doi.org/10.1155/2013/705878 |
| spellingShingle | Tom Burr Michael S. Hamada John Howell Misha Skurikhin Larry Ticknor Brian Weaver Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism Science and Technology of Nuclear Installations |
| title | Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism |
| title_full | Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism |
| title_fullStr | Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism |
| title_full_unstemmed | Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism |
| title_short | Estimating Alarm Thresholds for Process Monitoring Data under Different Assumptions about the Data Generating Mechanism |
| title_sort | estimating alarm thresholds for process monitoring data under different assumptions about the data generating mechanism |
| url | http://dx.doi.org/10.1155/2013/705878 |
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