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CERN Document Server 2,023 notices trouvées  1 - 10suivantfin  aller vers la notice: La recherche a duré 0.65 secondes. 
1.
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Integrating System Knowledge in Unsupervised Anomaly Detection Algorithms for Simulation-Based Failure Prediction of Electronic Circuits / Waldhauser, Felix (CERN ; U. Stuttgart (main)) ; Boukabache, Hamza (CERN) ; Perrin, Daniel (CERN) ; Roesler, Stefan (CERN) ; Dazer, Martin (U. Stuttgart (main))
Machine learning algorithms enable failure prediction of large-scale, distributed systems using historical time-series datasets. Although unsupervised learning algorithms represent a possibility to detect an evolving variety of anomalies, they do not provide links between detected data events and system failures. [...]
2023 - 8 p. - Published in : 10.18429/JACoW-ICALEPCS2023-TU1BCO02 Fulltext: PDF;
In : 19th International Conference on Accelerator and Large Experimental Physics Control Systems (ICALEPCS 2023), Cape Town, South Africa, 7 - 13 Oct 2023, pp.249-256
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Wavelet-based Noise Extraction for Anomaly Detection Applied to Safety-critical Electronics at CERN / Waldhauser, Felix (CERN ; Stuttgart U.) ; Boukabache, Hamza (CERN) ; Perrin, Daniel (CERN) ; Dazer, Martin (Stuttgart U.)
Due to the possible damage caused by unforeseen failures of safety-critical systems, it is crucial to maintain these systems appropriately to ensure high reliability and availability. If numerous units of a system are installed in various areas and permanent access is not guaranteed, remote, data-driven condition monitoring methods can be used to schedule maintenance actions and to prevent unexpected failures. [...]
2022 - 8 p. Fulltext: PDF;
In : 32nd European Safety and Reliability Conference (ESREL 2022), Dublin, Ireland, 28 Aug - 1 Sep 2022, pp.1844-1851
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3.
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Generating Realistic Failure Data for Predictive Maintenance: A Simulation and cGAN-based Methodology / Waldhauser, Felix (CERN ; Stuttgart U.) ; Boukabache, Hamza (CERN) ; Perrin, Daniel (CERN) ; Dazer, Martin (Stuttgart U.)
Absence of failure data is a common challenge for datadriven predictive maintenance, particularly in the context of new or highly reliable systems. This is especially problematic for system level failure prediction of analog electronics since failure characteristics depend on the actual system layout and thus might change with system upgrades. [...]
2024 - 2 p. Fulltext: PDF;
In : Proceedings of the European Conference of the PHM Society 2024, pp.1024-1026
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Anomaly detection in data sets generated by the CERN Radiation Monitoring Electronic system CROME to develop predictive maintenance algorithms / Waldhauser, Felix Johannes
Several experiments and particle accelerators at CERN, the largest laboratory of high-energy particle physics in the world, require accurate monitoring of ionizing stray radiation to protect people and the environment [...]
CERN-THESIS-2021-364 - 82 p.

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Unsupervised machine learning for detection of faulty beam position monitors / Fol, Elena (CERN ; Goethe U., Frankfurt (main)) ; Coello de Portugal, Jaime Maria (CERN) ; Tomás, Rogelio (CERN)
Unsupervised learning includes anomaly detection techniques that are suitable for the detection of unusual events such as instrumentation faults in particle accelerators. In this work we present the application of a decision trees-based algorithm to faulty BPMs detection at the LHC. [...]
CERN-ACC-2019-207.- 2019 - 4 p. - Published in : 10.18429/JACoW-IPAC2019-WEPGW081 Fulltext from publisher: PDF;
In : 10th International Particle Accelerator Conference, Melbourne, Australia, 19 - 24 May 2019, pp.WEPGW081
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Level-1 Trigger Calorimeter Image Convolutional Anomaly Detection Algorithm /CMS Collaboration
This performance note shows the implementation of the Calorimeter Image Convolutional Anomaly Detection Algorithm (CICADA), a Level-1 triggering algorithm designed to use fast, unsupervised machine learning techniques to trigger on anomalous events. Some of the score and rate performance of the algorithm as emulated on 2023 data are shown..
CMS-DP-2023-086; CERN-CMS-DP-2023-086.- Geneva : CERN, 2023 - 8 p. Fulltext: PDF;
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Repairable System Analysis of the Radioactive Ventilation Gas Monitors at CERN from Field Data / Hurst, Saskia Kristina (CERN) ; Boukabache, Hamza (CERN) ; Perrin, Daniel (CERN)
CERN-OPEN-2020-011.- Geneva : CERN, 2020 - 8 p. Article: PDF;
In : 5th European Conference of the Prognostics and Health Management Society , Turino, Italy, 27 - 31 Jul 2020
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Model-Independent Real-Time Anomaly Detection at the CMS Level-1 Calorimeter Trigger with CICADA /CMS Collaboration
In the search for new physics, real-time detection of anomalous events is critical for maximizing the discovery potential at the LHC. CICADA (Calorimeter Image Convolutional Anomaly Detection Algorithm) is a novel CMS trigger algorithm operating at the 40 MHz collision rate. [...]
CMS-DP-2024-121; CERN-CMS-DP-2024-121.- Geneva : CERN, 2024 - 21 p. Fulltext: PDF;
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Time Series Anomaly Detection for CERN Large-Scale Computing Infrastructure / Paltenghi, Matteo
Anomaly Detection in the CERN Data Center is a challenging task due to the large scale of the computing infrastructure and the large volume of data to monitor [...]
CERN-THESIS-2020-282 - Milano : Politecnico di Milano, 2020-10-02. - 119 p.

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Searches for new physics using unsupervised machine learning for anomaly detection in $\sqrt{s}$ = 13 TeV $pp$ collisions recorded by the ATLAS detector at the LHC / Wynne, Benjamin Michael (The University of Edinburgh (GB)) /ATLAS Collaboration
Various searches for new resonances using unsupervised machine learning for anomaly detection are presented. These searches look at two-body invariant masses including leptons, at a heavy resonance Y decaying into a Standard Model Higgs boson H and a new particle X in a fully hadronic final state, or at the masses of two jets..
ATL-PHYS-SLIDE-2024-321.- Geneva : CERN, 2024 - 36 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
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