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

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 a...

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Detalles Bibliográficos
Autores principales: Mancuso, Alessandro, Compare, Michele, Salo, Ahti, Zio, Enrico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Engineering
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646920/
https://www.ncbi.nlm.nih.gov/pubmed/31367666
http://dx.doi.org/10.1016/j.dib.2019.104243
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author Mancuso, Alessandro
Compare, Michele
Salo, Ahti
Zio, Enrico
author_facet Mancuso, Alessandro
Compare, Michele
Salo, Ahti
Zio, Enrico
author_sort Mancuso, Alessandro
collection PubMed
description 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).
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spelling pubmed-66469202019-07-31 Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system Mancuso, Alessandro Compare, Michele Salo, Ahti Zio, Enrico Data Brief Engineering 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). Elsevier 2019-07-08 /pmc/articles/PMC6646920/ /pubmed/31367666 http://dx.doi.org/10.1016/j.dib.2019.104243 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering
Mancuso, Alessandro
Compare, Michele
Salo, Ahti
Zio, Enrico
Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title_full Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title_fullStr Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title_full_unstemmed Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title_short Probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
title_sort probabilistic model data of time-dependent accident scenarios for a mixing tank mechanical system
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646920/
https://www.ncbi.nlm.nih.gov/pubmed/31367666
http://dx.doi.org/10.1016/j.dib.2019.104243
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AT saloahti probabilisticmodeldataoftimedependentaccidentscenariosforamixingtankmechanicalsystem
AT zioenrico probabilisticmodeldataoftimedependentaccidentscenariosforamixingtankmechanicalsystem

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