Int J Performability Eng ›› 2013, Vol. 9 ›› Issue (6): 715-728.doi: 10.23940/ijpe.13.6.p715.mag
• Original articles •
KEVIN J. WILSON, JOHN QUIGLEY, TIM BEDFORD, and LESLEY WALLS
Test and analysis plays a vital role in reducing uncertainty about the true performance of an engineering system. However tests can be expensive and designing an optimal test strategy can be challenging. We propose a Bayesian modelling process, which takes the form of a Bayesian Network, to determine anticipated test efficacy. Such a model supports engineering managers in assessing trade-offs between test resources and uncertainty reduction. Inference based on a full Bayesian model can be computationally demanding to the extent that it can limit practical application. To overcome this constraint, we develop a Bayes linear approximation for inference. This approach is known as a Bayes linear Bayes graphical model. After explaining the key principles of the method, we provide an application to a real industrial test to establish the condition of an ageing engineering system.
KEVIN J. WILSON, JOHN QUIGLEY, TIM BEDFORD, and LESLEY WALLS. Bayes Linear Bayes Graphical Models in the Design of Optimal Test Strategies [J]. Int J Performability Eng, 2013, 9(6): 715-728.
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