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000607017 041__ $$aEnglish
000607017 088__ $$2arXiv$$aarXiv:2405.12391
000607017 088__ $$2arXiv$$aarXiv:2405.12391
000607017 1001_ $$0P:(DE-H253)PIP1096696$$aSulc, Antonin$$b0$$eCorresponding author$$udesy
000607017 1112_ $$aThe 15th International Particle Accelerator Conference$$cNashville$$d2024-05-19 - 2024-05-24$$gIPAC'24$$wUSA
000607017 245__ $$aAutomated anomaly detection on European XFEL klystrons
000607017 260__ $$a[Geneva]$$bJACoW Publishing$$c2024
000607017 29510 $$a[Ebook] 15th International Particle Accelerator Conference, Nashville, Tennessee : May 19-24, 2024, Nashville, Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan , [Geneva] : JACoW Publishing, [2024],
000607017 300__ $$a3575 - 3578
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000607017 500__ $$a4 pages, 4 figures, 15, 15TH International Particle Accelerator Conference
000607017 520__ $$aHigh-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL. Exchanging these high-power components takes time and effort, thus it is necessary to minimize maintenance and downtime and at the same time maximize the device's operation. In an attempt to explore the behavior of klystrons using machine learning, we completed a series of experiments on our klystrons to determine various operational modes and conduct feature extraction and dimensionality reduction to extract the most valuable information about a normal operation. To analyze recorded data we used state-of-the-art data-driven learning techniques and recognized the most promising components that might help us better understand klystron operational states and identify early on possible faults or anomalies.
000607017 536__ $$0G:(DE-HGF)POF4-6G13$$a6G13 - Accelerator of European XFEL (POF4-6G13)$$cPOF4-6G13$$fPOF IV$$x0
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000607017 650_7 $$2Other$$aAccelerator Physics
000607017 650_7 $$2Other$$amc6-beam-instrumentation-controls-feedback-and-operational-aspects - MC6: Beam Instrumentation, Controls, Feedback, and Operational Aspects
000607017 650_7 $$2Other$$aMC6.T22 - MC6.T22 Reliability, Operability
000607017 650_7 $$2autogen$$aklystron
000607017 650_7 $$2autogen$$aoperation
000607017 650_7 $$2autogen$$aacceleration
000607017 650_7 $$2autogen$$atiming
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000607017 693__ $$0EXP:(DE-MLZ)NOSPEC-20140101$$5EXP:(DE-MLZ)NOSPEC-20140101$$eNo specific instrument$$x0
000607017 7001_ $$0P:(DE-H253)PIP1087213$$aEichler, Annika$$b1$$udesy
000607017 7001_ $$0P:(DE-H253)PIP1007238$$aWilksen, Tim$$b2$$udesy
000607017 773__ $$a10.18429/JACoW-IPAC2024-THPR36
000607017 7870_ $$0PUBDB-2025-01089$$aSulc, Antonin et.al.$$d2024$$iIsParent$$rarXiv:2405.12391$$tAutomated anomaly detection on European XFEL klystrons
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000607017 9101_ $$0I:(DE-588)1043621512$$6P:(DE-H253)PIP1087213$$aEuropean XFEL$$b1$$kXFEL.EU
000607017 9101_ $$0I:(DE-588b)2008985-5$$6P:(DE-H253)PIP1007238$$aDeutsches Elektronen-Synchrotron$$b2$$kDESY
000607017 9131_ $$0G:(DE-HGF)POF4-6G13$$1G:(DE-HGF)POF4-6G0$$2G:(DE-HGF)POF4-600$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Materie$$lGroßgeräte: Materie$$vAccelerator of European XFEL$$x0
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000607017 9201_ $$0I:(DE-H253)MCS_4-20120731$$kMCS 4$$lBeschleunigerkontrollen (FLASH/XFEL)$$x0
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