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Data-Driven Risk Matrices for CERN’s Accelerators

A risk matrix is a common tool used in risk assessment, defining risk levels with respect to the severity and probability of the occurrence of an undesired event. Risk levels can then be used for different purposes, e.g. defining subsystem reliability or personnel safety requirements. Over the histo...

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Autores principales: Cartier-Michaud, Thomas, Apollonio, Andrea, Blarasin, Gennaro, Todd, Benjamin, Uythoven, Jan
Lenguaje:eng
Publicado: JACoW 2021
Materias:
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2021-TUPAB325
http://cds.cern.ch/record/2811867
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author Cartier-Michaud, Thomas
Apollonio, Andrea
Blarasin, Gennaro
Todd, Benjamin
Uythoven, Jan
author_facet Cartier-Michaud, Thomas
Apollonio, Andrea
Blarasin, Gennaro
Todd, Benjamin
Uythoven, Jan
author_sort Cartier-Michaud, Thomas
collection CERN
description A risk matrix is a common tool used in risk assessment, defining risk levels with respect to the severity and probability of the occurrence of an undesired event. Risk levels can then be used for different purposes, e.g. defining subsystem reliability or personnel safety requirements. Over the history of the Large Hadron Collider (LHC), several risk matrices have been defined to guide system design. Initially, these were focused on machine protection systems, more recently these have also been used to prioritize consolidation activities. A new data-driven development of risk matrices for CERN’s accelerators is presented in this paper, based on data collected in the CERN Accelerator Fault Tracker (AFT). The data-driven approach improves the granularity of the assessment, and limits uncertainty in the risk estimation, as it is based on operational experience. In this paper the authors introduce the mathematical framework, based on operational failure data, and present the resulting risk matrix for LHC.
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institution Organización Europea para la Investigación Nuclear
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spelling cern-28118672022-06-10T23:32:21Zdoi:10.18429/JACoW-IPAC2021-TUPAB325http://cds.cern.ch/record/2811867engCartier-Michaud, ThomasApollonio, AndreaBlarasin, GennaroTodd, BenjaminUythoven, JanData-Driven Risk Matrices for CERN’s AcceleratorsAccelerators and Storage RingsA risk matrix is a common tool used in risk assessment, defining risk levels with respect to the severity and probability of the occurrence of an undesired event. Risk levels can then be used for different purposes, e.g. defining subsystem reliability or personnel safety requirements. Over the history of the Large Hadron Collider (LHC), several risk matrices have been defined to guide system design. Initially, these were focused on machine protection systems, more recently these have also been used to prioritize consolidation activities. A new data-driven development of risk matrices for CERN’s accelerators is presented in this paper, based on data collected in the CERN Accelerator Fault Tracker (AFT). The data-driven approach improves the granularity of the assessment, and limits uncertainty in the risk estimation, as it is based on operational experience. In this paper the authors introduce the mathematical framework, based on operational failure data, and present the resulting risk matrix for LHC.JACoWoai:cds.cern.ch:28118672021
spellingShingle Accelerators and Storage Rings
Cartier-Michaud, Thomas
Apollonio, Andrea
Blarasin, Gennaro
Todd, Benjamin
Uythoven, Jan
Data-Driven Risk Matrices for CERN’s Accelerators
title Data-Driven Risk Matrices for CERN’s Accelerators
title_full Data-Driven Risk Matrices for CERN’s Accelerators
title_fullStr Data-Driven Risk Matrices for CERN’s Accelerators
title_full_unstemmed Data-Driven Risk Matrices for CERN’s Accelerators
title_short Data-Driven Risk Matrices for CERN’s Accelerators
title_sort data-driven risk matrices for cern’s accelerators
topic Accelerators and Storage Rings
url https://dx.doi.org/10.18429/JACoW-IPAC2021-TUPAB325
http://cds.cern.ch/record/2811867
work_keys_str_mv AT cartiermichaudthomas datadrivenriskmatricesforcernsaccelerators
AT apollonioandrea datadrivenriskmatricesforcernsaccelerators
AT blarasingennaro datadrivenriskmatricesforcernsaccelerators
AT toddbenjamin datadrivenriskmatricesforcernsaccelerators
AT uythovenjan datadrivenriskmatricesforcernsaccelerators