CERN Accelerating science

Article
Title	Data-Driven Risk Matrices for CERN’s Accelerators
Related title	DATA-DRIVEN RISK MATRICES FOR CERN’S ACCELERATORS
Author(s)	Cartier-Michaud, Thomas (CERN) ; Apollonio, Andrea (CERN) ; Blarasin, Gennaro (CERN) ; Todd, Benjamin (CERN) ; Uythoven, Jan (CERN)
Publication	Geneva : JACoW, 2021
Number of pages	4
In:	JACoW IPAC 2021 (2021) 2260-2263
In:	12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.2260-2263
DOI	10.18429/JACoW-IPAC2021-TUPAB325 (publication)
Subject category	Accelerators and Storage Rings
Abstract	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.
Copyright/License	publication: © 2021 (License: CC-BY-3.0)

Corresponding record in: Inspire