Open Access
November 2006 Advances in Data Combination, Analysis and Collection for System Reliability Assessment
Alyson G. Wilson, Todd L. Graves, Michael S. Hamada, C. Shane Reese
Statist. Sci. 21(4): 514-531 (November 2006). DOI: 10.1214/088342306000000439


The systems that statisticians are asked to assess, such as nuclear weapons, infrastructure networks, supercomputer codes and munitions, have become increasingly complex. It is often costly to conduct full system tests. As such, we present a review of methodology that has been proposed for addressing system reliability with limited full system testing. The first approaches presented in this paper are concerned with the combination of multiple sources of information to assess the reliability of a single component. The second general set of methodology addresses the combination of multiple levels of data to determine system reliability. We then present developments for complex systems beyond traditional series/parallel representations through the use of Bayesian networks and flowgraph models. We also include methodological contributions to resource allocation considerations for system relability assessment. We illustrate each method with applications primarily encountered at Los Alamos National Laboratory.


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Alyson G. Wilson. Todd L. Graves. Michael S. Hamada. C. Shane Reese. "Advances in Data Combination, Analysis and Collection for System Reliability Assessment." Statist. Sci. 21 (4) 514 - 531, November 2006.


Published: November 2006
First available in Project Euclid: 23 April 2007

zbMATH: 1129.62093
MathSciNet: MR2395740
Digital Object Identifier: 10.1214/088342306000000439

Keywords: Bayesian , Bayesian network , Biased data , complex system , count data , degradation data , fault tree , flowgraph , Genetic algorithm , lifetime data , logistic regression , Markov chain Monte Carlo , Metropolis algorithm , multilevel data , nonhomogeneous Poisson process , prior elicitation , reliability block diagram , repairable system , resource allocation

Rights: Copyright © 2006 Institute of Mathematical Statistics

Vol.21 • No. 4 • November 2006
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