Advances in Data Combination, Analysis and Collection for System Reliability Assessment

Wilson, Alyson G. ; Graves, Todd L. ; Hamada, Michael S. ; Reese, C. Shane

Statist. Sci., Tome 21 (2006) no. 1, p. 514-531 / Harvested from Project Euclid

Résumé

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.

Détail

Publié le : 2006-11-14
Classification:  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