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).

Portfolio optimization of safety measures for the prevention of time-dependent accident scenarios

“…For instance, the squared number δ = 1 on the arc connecting Sprinkler to Ignition indicates the causal dependence of Ignition=Spark at time τ to the event Sprinkler=Activation at time τ − 1. For more details on the definition of the probabilistic model, please refer to our Data in Brief article [30].…”

“…Preventive safety measures reduce the expected disutility of the negative outcomes at the safety target Consq. Our Data in Brief article [30] reports the 18 preventive safety measures, including illustrative costs and updated failure probability of the components. The optimization model determines the entire set of non-dominated portfolios of preventive safety measures which minimize the expected disutility of the safety target Consq throughout multiple time stages.…”