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

Alessandro Mancuso; Michele Compare; Ahti Salo; Enrico Zio

doi:10.1016/j.dib.2019.104243

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

Data Brief. 2019 Jul 8:25:104243. doi: 10.1016/j.dib.2019.104243. eCollection 2019 Aug.

Authors

Alessandro Mancuso^{1

2}, Michele Compare^{2

3}, Ahti Salo¹, Enrico Zio^{2

3

4}

Affiliations

¹ Department of Mathematics and Systems Analysis, Aalto University, Finland.
² Department of Energy Engineering, Politecnico di Milano, Italy.
³ Aramis s.r.l, Milano, Italy.
⁴ MINES ParisTech, PSL Research University, CRC, Sophia Antipolis, France.

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: Dynamic bayesian networks; Portfolio optimization; Preventive safety measures; Risk analysis; System reliability.