Lifetime modelling with a Weibull law: comparison of three Bayesian Methods

For a nuclear power plant, being able to estimate the lifetime of important components is strategic. But data is usually insufficient to do so. Thus, it is relevant to use expertise, together with data, in order to assess the value of lifetime on the grounds of both sources. The Bayesian frame and the choice of a Weibull law to model the random time for replacement are relevant. They have been chosen for this article. Two indicators are computed : the mean lifetime of any component and the mean residual lifetime of a given component, after it has been controlled. Three different Bayesian methods are compared on three sets of data. The article shows that the three methods lead to coherent results and that uncertainties are strongly reduced. The method developed around PMC has two main advantages: it models a conditional dependence of the two parameters of the Weibull law, which enables more coherent results on the prior; it has a parameter that weights the strength of the expertise. This last point is very important to do lifetime assessments, because then, expertise is not used to increase too small samples as much as to do a real extrapolation, far beyond what data itself say. (authors)