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@inproceedings{cartier-michaud:ipac2021-tupab325,
author = {T. Cartier-Michaud and A. Apollonio and G.B. Blarasin and B. Todd and J.A. Uythoven},
title = {{Data-Driven Risk Matrices for CERN’s Accelerators}},
booktitle = {Proc. IPAC'21},
pages = {2260--2263},
eid = {TUPAB325},
language = {english},
keywords = {operation, proton, linac, synchrotron, machine-protect},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-TUPAB325},
url = {https://jacow.org/ipac2021/papers/tupab325.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-TUPAB325},
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.}},
}