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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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Works referenced in this record:

High breakdown estimation methods for Phase I multivariate control charts
journal, January 2007

  • Jensen, Willis A.; Birch, Jeffrey B.; Woodall, William H.
  • Quality and Reliability Engineering International, Vol. 23, Issue 5
  • DOI: 10.1002/qre.837

Robustness of mean E(X) and R charts
journal, April 1988

  • Chan, L. K.; Hapuarachchi, K. P.; Macpherson, B. D.
  • IEEE Transactions on Reliability, Vol. 37, Issue 1
  • DOI: 10.1109/24.3728

Signal estimation and change detection in tank data for nuclear safeguards
journal, June 2011

  • Burr, Tom; Suzuki, Mitsutoshi; Howell, John
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 640, Issue 1
  • DOI: 10.1016/j.nima.2011.02.078

Model-Based Clustering, Discriminant Analysis, and Density Estimation
journal, June 2002

  • Fraley, Chris; Raftery, Adrian E.
  • Journal of the American Statistical Association, Vol. 97, Issue 458
  • DOI: 10.1198/016214502760047131

Phase I control chart based on a kernel estimator of the quantile function
journal, May 2011

  • Mercado, Gary R.; Conerly, Michael D.; Perry, Marcus B.
  • Quality and Reliability Engineering International, Vol. 27, Issue 8
  • DOI: 10.1002/qre.1201

Study on Loss Detection Algorithms Using Tank Monitoring Data
journal, January 2009

  • Suzuki, Mitsutoshi; Hori, Masato; Nagaoka, Shinichi
  • Journal of Nuclear Science and Technology, Vol. 46, Issue 2
  • DOI: 10.3327/jnst.46.184

On the Estimation of the Extreme-Value Index and Large Quantile Estimation
journal, December 1989

  • Dekkers, Arnold L. M.; Haan, Laurens De
  • The Annals of Statistics, Vol. 17, Issue 4
  • DOI: 10.1214/aos/1176347396

Parametric control charts
journal, August 2004

  • Albers, Willem; Kallenberg, Wilbert C. M.; Nurdiati, Sri
  • Journal of Statistical Planning and Inference, Vol. 124, Issue 1
  • DOI: 10.1016/S0378-3758(03)00200-3

Estimating the probability of a rare event
journal, April 1999

  • Sinha, Ashoke Kumar; de Haan, Laurens
  • The Annals of Statistics, Vol. 27, Issue 2
  • DOI: 10.1214/aos/1018031214

Electrorefiner Liquid Cadmium Cathode Crucible Thermal Shock
journal, June 2006


Maintaining the performance of a learned classifier under concept drift
journal, December 1999


Loss detection results on simulated tank data modified by realistic effects
journal, February 2012


Pyroprocessing Flowsheets for Recycling used Nuclear fuel
journal, August 2011


Self-adapting control charts
journal, August 2006


Run length, average run length and false alarm rate of shewhart x-bar chart: exact derivations by conditioning
journal, January 2000


Shewhart x-charts with estimated process variance
journal, January 1981

  • Ghosh, B. K.; Reynolds, Marion R.; Yer, Van Hui
  • Communications in Statistics - Theory and Methods, Vol. 10, Issue 18
  • DOI: 10.1080/03610928108828152

Adjusted empirical likelihood method for quantiles
journal, December 2003

  • Zhou, Wang; Jing, Bing-Yi
  • Annals of the Institute of Statistical Mathematics, Vol. 55, Issue 4
  • DOI: 10.1007/BF02523389

New Corrections for Old Control Charts
journal, July 2005

  • Albers, Willem; Kallenberg, Wilbert C. M.
  • Quality Engineering, Vol. 17, Issue 3
  • DOI: 10.1081/QEN-200063498

Estimating alarm thresholds and the number of components in mixture distributions
journal, September 2012

  • Burr, Tom; Hamada, Michael S.
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 685
  • DOI: 10.1016/j.nima.2012.05.035

Likelihood-based procedures for threshold diagnostics and uncertainty in extreme value modelling: Extreme Value Modelling
journal, February 2012

  • Wadsworth, J. L.; Tawn, J. A.
  • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 74, Issue 3
  • DOI: 10.1111/j.1467-9868.2011.01017.x

On the estimation of extreme tail probabilities
journal, June 1997