Publications

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.

Abstract not provided.

To determine whether a component is meeting its reliability requirement during production, acceptance sampling is employed in which selected units coming off the production line are subjected to additional environmental and/or destructive tests that are within the normal environment space to which the component is expected to be exposed throughout its life in the Stockpile. This report describes what these tests are and how they are scored for reliability purposes. The roles of screens, Engineering Use Only tests, and next assembly product acceptance testing are also discussed, along with both the advantages and disadvantages of environmental and destructive testing.