Bayesian Updating of the Material Balances Covariance Matrices with Training Data

Tom Burr and Michael S. Hamada
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
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bayesian update cov matrix-rev3-july17.pdf299.99 KBJuly 18, 2014 - 2:03pm

The main quantitative measure of nuclear safeguards effectiveness is nuclear material accounting (NMA), which consists of sequences of measured material balances that should be close to zero if there is no loss of special nuclear material such as Pu. NMA is essentially ``accounting with measurement errors,'' which arise from good, but imperfect, measurements.
The covariance matrix $\Sigma_{MB}$ of a sequence of material balances is the key quantity that determines the probability to detect loss.
There is a recent push to include process monitoring (PM) data along with material balances from NMA in new schemes to monitor for material loss. PM data includes near-real-time measurements by the operator to monitor and control process operations. One concern regarding PM data is the need to estimate normal behavior of PM residuals, which requires a training period prior to ongoing testing for material loss. Assuming that a training period is used for PM data prior to its use in statistical testing for loss, that same training period could also be used for improving the estimate of $\Sigma_{MB}$ that is used in NMA.
We consider updating $\Sigma_{MB}$ using training data with a Bayesian approach.
A simulation study assesses the improvement gained with larger amounts of training data.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
006
Page Count: 
13
Submission Keywords: 
nuclear material accounting; process monitoring; Bayesian updating of covariance matrix
Submission Topic Areas: 
Uncertainty Quantification and Management in PHM
Submitted by: 
  
 
 
 

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