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CERN Document Server 找到 9 筆記錄  檢索需時 0.61 秒. 
1.
Data Augmentation for Breakdown Prediction in CLIC RF Cavities / Bovbjerg, Holger (CERN ; Aalborg U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Millar, William (CERN) ; Obermair, Christoph (CERN ; Graz, Tech. U.) ; Shen, Ming (CERN ; Aalborg U.) ; Tan, Zheng-Hua (CERN ; Aalborg U.) ; Wollmann, Daniel (CERN)
One of the primary limitations on the achievable accelerating gradient in normal-conducting accelerator cavities is the occurrence of vacuum arcs, also known as RF breakdowns. A recent study on experimental data from the CLIC XBOX2 test stand at CERN proposes the use of supervised machine learning methods for predicting RF breakdowns. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 1553-1556 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.1553-1556
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2.
Reliability Analysis of the HL-LHC Energy Extraction System / Blaszkiewicz, Milosz (CERN) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Panev, Bozhidar Ivanov (CERN) ; Pojer, Mirko (CERN) ; Wollmann, Daniel (CERN)
The energy extraction systems for the protection of the new HL-LHC superconducting magnet circuits are based on vacuum breakers. This technology allows to significantly reduce the switch opening time and increases the overall system reliability with reduced maintenance needs. [...]
2022 - 4 p. - Published in : JACoW IPAC 2022 (2022) 747-750 Fulltext: PDF;
In : 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12 - 17 Jun 2022, pp.747-750
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3.
Availability Modeling of the Solid-State Power Amplifiers for the CERN SPS RF Upgrade / Felsberger, Lukas (CERN) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Montesinos, Eric (CERN) ; Oliveira, Joao Carlos (CERN) ; Uythoven, Jan (CERN)
As part of the LHC Injector Upgrade program a complete overhaul of the Super Proton Synchrotron Radio-Frequency (RF) system took place. New cavities have been installed for which the solid-state technology was chosen to deliver a combined RF power of 2 MW from 2560 RF amplifiers. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 2308-2311 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.2308-2311
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4.
Data-Driven Risk Matrices for CERN’s Accelerators / Cartier-Michaud, Thomas (CERN) ; Apollonio, Andrea (CERN) ; Blarasin, Gennaro (CERN) ; Todd, Benjamin (CERN) ; Uythoven, Jan (CERN)
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. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 2260-2263 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.2260-2263
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5.
Machine Learning Models for Breakdown Prediction in RF Cavities for Accelerators / Obermair, Christoph (CERN ; Graz, Tech. U.) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Catalán Lasheras, Nuria (CERN) ; Felsberger, Lukas (CERN) ; Millar, William L (CERN) ; Pernkopf, Franz (CERN ; Graz, Tech. U.) ; Wuensch, Walter (CERN)
Radio Frequency (RF) breakdowns are one of the most prevalent limits in RF cavities for particle accelerators. During a breakdown, field enhancement associated with small deformations on the cavity surface results in electrical arcs. [...]
Geneva : JACoW, 2021 - 4 p. - Published in : JACoW IPAC 2021 (2021) 1068-1071 Fulltext: PDF;
In : 12th International Particle Accelerator Conference (IPAC 2021), Online, 24 - 28 May 2021, pp.1068-1071
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6.
Explainable Machine Learning for Breakdown Prediction in High Gradient RF Cavities / Obermair, Christoph (CERN ; Graz U.) ; Cartier-Michaud, Thomas (CERN) ; Apollonio, Andrea (CERN) ; Millar, William (CERN) ; Felsberger, Lukas (CERN) ; Fischl, Lorenz (CERN) ; Bovbjerg, Holger Severin (CERN) ; Wollmann, Daniel (CERN) ; Wuensch, Walter (CERN) ; Catalan-Lasheras, Nuria (CERN) et al.
The occurrence of vacuum arcs or radio frequency (rf) breakdowns is one of the most prevalent factors limiting the high-gradient performance of normal conducting rf cavities in particle accelerators. In this paper, we search for the existence of previously unrecognized features related to the incidence of rf breakdowns by applying a machine learning strategy to high-gradient cavity data from CERN's test stand for the Compact Linear Collider (CLIC). [...]
arXiv:2202.05610.- 2022-10-03 - 18 p. - Published in : Phys. Rev. Accel. Beams 25 (2022) 104601 Fulltext: PDF;
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7.
Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case / Felsberger, Lukas (Ludwig Maximilians Universitat (DE)) ; Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Todd, Benjamin (CERN) ; Müller, Andreas (Hochschule Darmstadt) ; Kranzlmüller, Dieter (Ludwig-Maximilians-Universität Muenchen) ; Apollonio, Andrea (CERN)
Sophisticated infrastructures often exhibit misbehaviour and failures resulting from complex interactions of their constituent subsystems. Such infrastructures use alarms, event and fault information, which is recorded to help diagnose and repair failure conditions by operations experts. [...]
CERN-ACC-2020-0016.- Geneva : CERN, 2020 - 20 p. Fulltext: PDF;
In : , Dublin, Ireland, 25 - 28 Aug 2020, pp.139-158
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8.
Machine learning for early fault detection in accelerator systems / Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Felsberger, Lukas (Ludwig Maximilians Universitat (DE)) ; Mueller, Andreas (University of Applied Sciences Darmstadt (DE)) ; Todd, Benjamin (CERN)
With the development of systems based on a combination of mechanics, electronics and – more and more - software components, increasing system complexity is a de facto trend in the engineering world. [...]
CERN-ACC-NOTE-2020-0005. - 2020. - 22 p.
Full text - Full text
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9.
Machine learning for early fault detection in accelerator systems / Apollonio, Andrea (CERN) ; Cartier-Michaud, Thomas (CERN) ; Felsberger, Lukas (Ludwig Maximilians Universitat (DE)) ; Mueller, Andreas (University of Applied Sciences Darmstadt (DE)) ; Todd, Benjamin (CERN)
With the development of systems based on a combination of mechanics, electronics and – more and more - software components, increasing system complexity is a de facto trend in the engineering world. [...]
CERN-ACC-2020-0004. - 2020. - 22 p.
Full text
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