Quantifying reliability uncertainty from catastrophic and margin defects: A proof of concept
| Autor: | Aparna V. Huzurbazar, Stephen V. Crowder, Christine M. Anderson-Cook, John F. Lorio, James T. Ringland, Alyson G. Wilson |
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| Rok vydání: | 2011 |
| Předmět: |
Engineering
business.industry Bayesian probability Industrial and Manufacturing Engineering Reliability engineering Proof of concept Margin (machine learning) Component (UML) Sensitivity analysis Uncertainty quantification Safety Risk Reliability and Quality business Uncertainty analysis Reliability (statistics) |
| Zdroj: | Reliability Engineering & System Safety. 96:1063-1075 |
| ISSN: | 0951-8320 |
| Popis: | We aim to analyze the effects of component level reliability data, including both catastrophic failures and margin failures, on system level reliability. While much work has been done to analyze margins and uncertainties at the component level, a gap exists in relating this component level analysis to the system level. We apply methodologies for aggregating uncertainty from component level data to quantify overall system uncertainty. We explore three approaches towards this goal, the classical Method of Moments (MOM), Bayesian, and Bootstrap methods. These three approaches are used to quantify the uncertainty in reliability for a system of mixed series and parallel components for which both pass/fail and continuous margin data are available. This paper provides proof of concept that uncertainty quantification methods can be constructed and applied to system reliability problems. In addition, application of these methods demonstrates that the results from the three fundamentally different approaches can be quite comparable. |
| Databáze: | OpenAIRE |
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