IPAC2021 - List of Keywords (machine-protect)

Paper	Title	Other Keywords	Page
MOPAB022	FailSim: A Numerical Toolbox for the Study of Fast Failures and Their Impact on Machine Protection at the CERN Large Hadron Collider	simulation, optics, beam-losses, quadrupole	111
	C. Hernalsteens, G. Sterbini, O.K. Tuormaa, C. Wiesner, D. Wollmann CERN, Meyrin, Switzerland
	The High Luminosity LHC (HL-LHC) foresees to reach a nominal, levelled luminosity of 5·10³⁴ cm^-2 s⁻¹ through a higher beam brightness and by using new equipment, such as larger aperture final focusing quadrupole magnets. The HL-LHC upgrade has critical impacts on the machine protection strategy, as the stored beam energy reaches 700 MJ for each of the two beams. Some failure modes of the novel active superconducting magnet protection system of the inner triplet magnets, namely the Coupling-Loss Induced Quench (CLIQ) systems, have been identified as critical. This paper reports on FailSim, a Python-language framework developed to study the machine protection impact of failure cases and their proposed mitigation. It provides seamless integration of the successive phases required by the simulation studies, i.e., verifying the optics, preparing and running a MAD-X instance for multiple particle tracking, processing and analysing the simulation results and summarising them with the relevant plots to provide a solid estimate of the beam losses, their location and time evolution. The paper also presents and discusses the result of its application on the spurious discharge of a CLIQ unit.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB022
About •	paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 18 August 2021
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MOPAB345	Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC	monitoring, operation, superconducting-magnet, dipole	1072
	C. Obermair, A. Apollonio, Z. Charifoulline, M. Maciejewski, A.P. Verweij CERN, Geneva, Switzerland C. Obermair, F. Pernkopf TUG, Graz, Austria
	The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. In this paper, we derive a model that can extend the existing signal monitoring applications for the LHC protection systems with machine learning. Our hybrid model combines an existing threshold-based system with a SVM by using signals, manually validated by experts. Even with a limited amount of data, the SVM learns to integrate the expert knowledge and contributes to a better classification of safety-critical signals. Using this approach, we analyze historical signals of quench heaters, which are an important part of the quench protection system for superconducting magnets. Particularly, it is possible to incorporate expert decisions into the classification process and to improve the failure detection rate of the existing quench heater discharge analysis tool.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345
About •	paper received ※ 20 May 2021 paper accepted ※ 19 July 2021 issue date ※ 01 September 2021
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TUPAB282	Optical Beam Loss Monitor Based on Fibres for Beam Loss Monitoring and RF Breakdown Detection	diagnostics, synchrotron, experiment, operation	2136
	N. Kumar, C.P. Welsch, J. Wolfenden The University of Liverpool, Liverpool, United Kingdom N. Kumar, C.P. Welsch, J. Wolfenden Cockcroft Institute, Warrington, Cheshire, United Kingdom
	Funding: This project has received funding from STFC under grant reference ST/V001302/1. Standard beam loss monitors are used to detect losses at specific locations which is not a practical solution for loss monitoring throughout the whole beam-line. Optical fibre beam loss monitors (oBLMs) are based on the detection of Cherenkov radiation from high energy charged particles having the advantage of covering more than 100 m of an accelerator with a single detector. This system was successfully installed at the Australian Synchrotron covering the entire facility for beam loss measurements. Successful measurements were also demonstrated on the Compact Linear Accelerator for Research and Applications (CLARA), UK with sub-metre beam loss resolution. oBLMs are non-invasive monitors for the detection of the beam loss and RF breakdown within particle accelerators, which has been developed by the QUASAR Group based at the Cockcroft Institute/University of Liverpool, UK in collaboration of D-Beam Ltd, UK. This paper discusses the overview of the system, the incorporation of the monitor into the accelerator diagnostic system, calibration experiment of oBLM and future plans for the system.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB282
About •	paper received ※ 19 May 2021 paper accepted ※ 02 June 2021 issue date ※ 10 August 2021
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TUPAB325	Data-Driven Risk Matrices for CERN’s Accelerators	operation, proton, linac, synchrotron	2260
	T. Cartier-Michaud, A. Apollonio, G.B. Blarasin, B. Todd, J.A. Uythoven CERN, Geneva, Switzerland
	Funding: Research supported by the HL-LHC project. 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.
	Poster TUPAB325 [0.499 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB325
About •	paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

FailSim: A Numerical Toolbox for the Study of Fast Failures and Their Impact on Machine Protection at the CERN Large Hadron Collider

simulation, optics, beam-losses, quadrupole

111

C. Hernalsteens, G. Sterbini, O.K. Tuormaa, C. Wiesner, D. Wollmann
CERN, Meyrin, Switzerland

The High Luminosity LHC (HL-LHC) foresees to reach a nominal, levelled luminosity of 5·10³⁴ cm^-2 s⁻¹ through a higher beam brightness and by using new equipment, such as larger aperture final focusing quadrupole magnets. The HL-LHC upgrade has critical impacts on the machine protection strategy, as the stored beam energy reaches 700 MJ for each of the two beams. Some failure modes of the novel active superconducting magnet protection system of the inner triplet magnets, namely the Coupling-Loss Induced Quench (CLIQ) systems, have been identified as critical. This paper reports on FailSim, a Python-language framework developed to study the machine protection impact of failure cases and their proposed mitigation. It provides seamless integration of the successive phases required by the simulation studies, i.e., verifying the optics, preparing and running a MAD-X instance for multiple particle tracking, processing and analysing the simulation results and summarising them with the relevant plots to provide a solid estimate of the beam losses, their location and time evolution. The paper also presents and discusses the result of its application on the spurious discharge of a CLIQ unit.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB022

About •

paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 18 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPAB345

Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC

monitoring, operation, superconducting-magnet, dipole

1072

C. Obermair, A. Apollonio, Z. Charifoulline, M. Maciejewski, A.P. Verweij
CERN, Geneva, Switzerland
C. Obermair, F. Pernkopf
TUG, Graz, Austria

The LHC is the world’s largest particle accelerator and uses a complex set of sophisticated and highly reliable machine protection systems to ensure a safe operation with high availability for particle physics production. The data gathered during several years of successful operation allow the use of data-driven methods to assist experts in finding anomalies in the behavior of those protection systems. In this paper, we derive a model that can extend the existing signal monitoring applications for the LHC protection systems with machine learning. Our hybrid model combines an existing threshold-based system with a SVM by using signals, manually validated by experts. Even with a limited amount of data, the SVM learns to integrate the expert knowledge and contributes to a better classification of safety-critical signals. Using this approach, we analyze historical signals of quench heaters, which are an important part of the quench protection system for superconducting magnets. Particularly, it is possible to incorporate expert decisions into the classification process and to improve the failure detection rate of the existing quench heater discharge analysis tool.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345

About •

paper received ※ 20 May 2021 paper accepted ※ 19 July 2021 issue date ※ 01 September 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB282

Optical Beam Loss Monitor Based on Fibres for Beam Loss Monitoring and RF Breakdown Detection

diagnostics, synchrotron, experiment, operation

2136

N. Kumar, C.P. Welsch, J. Wolfenden
The University of Liverpool, Liverpool, United Kingdom
N. Kumar, C.P. Welsch, J. Wolfenden
Cockcroft Institute, Warrington, Cheshire, United Kingdom

Funding: This project has received funding from STFC under grant reference ST/V001302/1.
Standard beam loss monitors are used to detect losses at specific locations which is not a practical solution for loss monitoring throughout the whole beam-line. Optical fibre beam loss monitors (oBLMs) are based on the detection of Cherenkov radiation from high energy charged particles having the advantage of covering more than 100 m of an accelerator with a single detector. This system was successfully installed at the Australian Synchrotron covering the entire facility for beam loss measurements. Successful measurements were also demonstrated on the Compact Linear Accelerator for Research and Applications (CLARA), UK with sub-metre beam loss resolution. oBLMs are non-invasive monitors for the detection of the beam loss and RF breakdown within particle accelerators, which has been developed by the QUASAR Group based at the Cockcroft Institute/University of Liverpool, UK in collaboration of D-Beam Ltd, UK. This paper discusses the overview of the system, the incorporation of the monitor into the accelerator diagnostic system, calibration experiment of oBLM and future plans for the system.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB282

About •

paper received ※ 19 May 2021 paper accepted ※ 02 June 2021 issue date ※ 10 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB325

Data-Driven Risk Matrices for CERN’s Accelerators

operation, proton, linac, synchrotron

2260

T. Cartier-Michaud, A. Apollonio, G.B. Blarasin, B. Todd, J.A. Uythoven
CERN, Geneva, Switzerland

Funding: Research supported by the HL-LHC project.
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.

Poster TUPAB325 [0.499 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB325

About •

paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 17 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)