Quantifying reliability uncertainty from catastrophic and margin defects: A proof of concept |
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Authors: | Christine M Anderson-Cook Stephen Crowder Aparna V Huzurbazar John Lorio James Ringland Alyson G Wilson |
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Affiliation: | a Statistical Sciences Group, Los Alamos National Laboratory, P.O. Box 1663, MS F600 Los Alamos, NM 87545, USA b Sandia National Laboratories, Albuquerque, NM 87185, USA c Sandia National Laboratories, Livermore, CA 94550, USA d Department of Statistics, Iowa State University, Ames, IA 50011, USA |
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Abstract: | 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. |
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Keywords: | Method of moments Bayesian analysis Bootstrap System reliability Catastrophic and margins failure modes |
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