Abstract
AbstractTo assess the reliability of a complex system, many different types of data may be available. Full‐system tests are the most direct measure of reliability, but may be prohibitively expensive or difficult to obtain. Other less direct measures, such as component or section level tests, may be cheaper to obtain and more readily available. Using a single Bayesian analysis, multiple sources of data can be combined to give component and system reliability estimates. Resource allocation looks to develop methods to predict which new data would most improve the precision of the estimate of system reliability, in order to maximally improve understanding. In this paper, we consider a relatively simple system with different types of data from the components and system. We present a methodology for assessing the relative improvement in system reliability estimation for additional data from the various types. Various metrics for comparing improvement and a response surface approach to modeling the relationship between improvement and the additional data are presented. Copyright © 2008 John Wiley & Sons, Ltd.
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