Abstract
Recent advances in computational capabilities often make engineering simula-tions of lifetime tractable. We consider the case in which there exist lifetime data from a computational model as well as data from a physical reliability ex-periment. In addition, there may also exist one or more expert opinions about the expected lifetime for selected factor settings. We simultaneously analyze the combined data using a hierarchical Bayes model. In this integrated approach we recognize important differences, such as possible biases, in these experimental data and expert opinions.
We illustrate the methodology by means of an example. Hellstrand [6] designed and conducted an experimentto study the effect of three categorical design parameters on ball bearing lifetime. In addition to the lifetime data from a 23 full factorial experiment. we assume the existence of computationally produced lifetimes for four of the eight factor settings for the same three factors. We also assume there are expert opinion data for seven of the eight factor settings. The integrated data are used to estimate the reliability functions for the eight factor settings. The results indicate that reliability is more precisely estimated by using this integrated data approach.
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Wilson, A.G., Reese, C.S., Hamada, M.S., Martz, H.F. (2004). Integrated Analysis of Computer and Physical Experimental Lifetime Data. In: Soyer, R., Mazzuchi, T.A., Singpurwalla, N.D. (eds) Mathematical Reliability: An Expository Perspective. International Series in Operations Research & Management Science, vol 67. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-9021-1_9
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DOI: https://doi.org/10.1007/978-1-4419-9021-1_9
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