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The Detection of Nuclear Materials Losses *
Author(s) -
Prasad Sameer,
Booth David,
Hu Michael Y.,
Deligonul Seyda
Publication year - 1995
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1995.tb01429.x
Subject(s) - cusum , outlier , control chart , computer science , dependency (uml) , identification (biology) , range (aeronautics) , chart , joint (building) , autoregressive–moving average model , data mining , statistics , reliability engineering , mathematics , process (computing) , artificial intelligence , engineering , autoregressive model , architectural engineering , botany , biology , aerospace engineering , operating system
The identification and location of materials losses in nuclear facilities is an important issue. Many complexities arise in monitoring such losses. These complexities include the dependency among materials balance observations and the influence of errors (outliers) on parameter estimates of various monitoring methods. The proposed Joint Estimation procedure is superior to standard methods (control chart and CUSUM) and to methods that build in correlation (ARMA control chart, ARMA CUSUM, and the Generalized M procedure) in the detection of nuclear materials losses. The Joint Estimation procedure is robust to the influence of outliers, is flexible in accommodating a range of dependencies among observations, and provides information on the type of loss. Further, the procedure is reliable in that it yields a probability of false alarms and a probability of detecting losses closer to specifications.