A case study for quantifying system reliability and uncertainty

Alyson G. Wilson, Christine M. Anderson-Cook and Aparna V. Huzurbazar

Reliability Engineering and System Safety, 2011, vol. 96, issue 9, 1076-1084

Abstract: The ability to estimate system reliability with an appropriate measure of associated uncertainty is important for understanding its expected performance over time. Frequently, obtaining full-system data is prohibitively expensive, impractical, or not permissible. Hence, methodology which allows for the combination of different types of data at the component or subsystem levels can allow for improved estimation at the system level. We apply methodologies for aggregating uncertainty from component-level data to estimate system reliability and quantify its overall uncertainty. This paper provides a proof-of-concept that uncertainty quantification methods using Bayesian methodology can be constructed and applied to system reliability problems for a system with both series and parallel structures.

Keywords: Bayesian; Multilevel data; Reliability block diagram; Monte Carlo (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:96:y:2011:i:9:p:1076-1084

DOI: 10.1016/j.ress.2010.09.012

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