Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system

Abstract

This article presents the risk assessment of a mixing tank mechanical system based on the failure probabilities of the components. Possible component failures can cause accidents which evolve over multiple time stages and can lead to system failure. The consequences of these accident scenarios are analyzed by quantifying the failure probabilities and severity of their outcomes. Illustrative costs and updated failure probabilities are provided to evaluate preventive safety measures. Data refers to the results of the Bayesian model presented in our research article (Mancuso et al., 2019). Keywords: Risk analysis, System reliability, Preventive safety measures, Dynamic bayesian networks, Portfolio optimization