A dynamic Bayesian networks modeling of human factors on offshore blowouts

An application of dynamic Bayesian networks for quantitative risk assessment of human factors on offshore blowouts is presented. Human error is described using human factor barrier failure (HFBF), which consists of three categories of factors, including individual factor barrier failure (IFBF), organizational factor barrier failure (OFBF) and group factor barrier failure (GFBF). The structure of human factors is illustrated using pseudo-fault tree, which is defined by incorporating the intermediate options into fault tree in order to eliminate the binary restriction. A methodology of translating pseudo-fault tree into Bayesian networks and dynamic Bayesian networks taking repair into consideration is proposed and the propagation is performed. The results show that the human factor barrier failure probability only increases within the first two weeks and rapidly reaches a stable level when the repair is considered, whereas it increases continuously when the repair action is not considered. The results of mutual information show that the important degree sequences for the three categories of human factors on HFBF are: GFBF, OFBF and IFBF. In addition, each individual human factor contributes different to the HFBF, those which contribute much should given more attention in order to improve the human reliability and prevent the potential accident occurring.

► A methodology for quantitative risk assessment of human factors is proposed.
► The pseudo-fault tree is translated into dynamic Bayesian network directly.
► The periodic test and subsequent repair are considered for modeling.
► Repair of failed components can improve the human reliability greatly.
► Each human factor contributes different to the human factor barrier failure.