Bayesian analysis of heterogeneous reliability data for vulnerability assessment. Issue 1 (2nd January 2016) - Iešmantas, Tomas
Abstract: Bayesian models are already present in almost every branch of reliability theory and its applications. This class of models provides an easy and intuitive way to infer about the issue at hand with a clear handling of data uncertainty. It is often the case that data used for the systems reliability assessment come from more than one information source. Whether it is power plants at different geographical locations, gas transmission pipelines operating in different environment or power transmission networks deployed within various areas. Therefore, different operating conditions, varying maintenance programs and efficiencies have their share in influencing the vulnerability and variability of reliability data. However, in practice, it is usually the case that this heterogeneity is neglected leading to the underestimation of underlying uncertainty of the data. Bayesian models are capable of dealing with this kind of uncertainty as opposed to the frequentists' statistical methods. Hierarchical Bayesian modelling technique provides means to quantify not only within-source, but also between-source uncertainties. Even in the case of small data samples, it performs well, unlike for example the classical likelihood method which may provide degenerate estimates. In this paper, authors investigate the possibility to incorporate this kind of uncertainty into the systems reliability and vulnerability assessment through the Bayesian framework in several cases: instrumentation and control components of nuclear power plants, gas transmission networks and power transmission grids.