Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards
Abstract
The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.
- Authors:
-
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1193445
- Report Number(s):
- LA-UR-14-27526
Journal ID: ISSN 1996-1073; ENERGA; PII: en8010501
- Grant/Contract Number:
- AC52-06NA25396
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Energies
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 1; Journal ID: ISSN 1996-1073
- Publisher:
- MDPI AG
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 97 MATHEMATICS AND COMPUTING; 98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL PROTECTION; data driven; hybrid method; nuclear material accounting; nuclear safeguards; period driven; process monitoring; residuals; statistical methods; time series; hybrid statistical testing; pattern recognition
Citation Formats
Burr, Tom, Hamada, Michael S., Ticknor, Larry, and Sprinkle, James. Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards. United States: N. p., 2015.
Web. doi:10.3390/en8010501.
Burr, Tom, Hamada, Michael S., Ticknor, Larry, & Sprinkle, James. Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards. United States. https://doi.org/10.3390/en8010501
Burr, Tom, Hamada, Michael S., Ticknor, Larry, and Sprinkle, James. 2015.
"Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards". United States. https://doi.org/10.3390/en8010501. https://www.osti.gov/servlets/purl/1193445.
@article{osti_1193445,
title = {Hybrid statistical testing for nuclear material accounting data and/or process monitoring data in nuclear safeguards},
author = {Burr, Tom and Hamada, Michael S. and Ticknor, Larry and Sprinkle, James},
abstractNote = {The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.},
doi = {10.3390/en8010501},
url = {https://www.osti.gov/biblio/1193445},
journal = {Energies},
issn = {1996-1073},
number = 1,
volume = 8,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}
Web of Science
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