Quantifying reliability uncertainty : a proof of concept.

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This paper develops Classical and Bayesian methods for quantifying the uncertainty in reliability for a system of mixed series and parallel components for which both go/no-go and variables data are available. Classical methods focus on uncertainty due to sampling error. Bayesian methods can explore both sampling error and other knowledge-based uncertainties. To date, the reliability community has focused on qualitative statements about uncertainty because there was no consensus on how to quantify them. This paper provides a proof of concept that workable, meaningful quantification methods can be constructed. In addition, the application of the methods demonstrated that the results from ... continued below

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63 p.

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Diegert, Kathleen V.; Dvorack, Michael A.; Ringland, James T.; Mundt, Michael Joseph; Huzurbazar, Aparna (Los Alamos National Laboratory, Los Alamos, NM); Lorio, John F. et al. October 1, 2009.

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This paper develops Classical and Bayesian methods for quantifying the uncertainty in reliability for a system of mixed series and parallel components for which both go/no-go and variables data are available. Classical methods focus on uncertainty due to sampling error. Bayesian methods can explore both sampling error and other knowledge-based uncertainties. To date, the reliability community has focused on qualitative statements about uncertainty because there was no consensus on how to quantify them. This paper provides a proof of concept that workable, meaningful quantification methods can be constructed. In addition, the application of the methods demonstrated that the results from the two fundamentally different approaches can be quite comparable. In both approaches, results are sensitive to the details of how one handles components for which no failures have been seen in relatively few tests.

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63 p.

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  • Report No.: SAND2009-2173
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/970305 | External Link
  • Office of Scientific & Technical Information Report Number: 970305
  • Archival Resource Key: ark:/67531/metadc929757

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  • October 1, 2009

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 30, 2016, 7:09 p.m.

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Diegert, Kathleen V.; Dvorack, Michael A.; Ringland, James T.; Mundt, Michael Joseph; Huzurbazar, Aparna (Los Alamos National Laboratory, Los Alamos, NM); Lorio, John F. et al. Quantifying reliability uncertainty : a proof of concept., report, October 1, 2009; United States. (digital.library.unt.edu/ark:/67531/metadc929757/: accessed October 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.