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@INPROCEEDINGS{Sulc:607017,
      author       = {Sulc, Antonin and Eichler, Annika and Wilksen, Tim},
      title        = {{A}utomated anomaly detection on {E}uropean {XFEL}
                      klystrons},
      address      = {[Geneva]},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2024-01722, arXiv:2405.12391. arXiv:2405.12391},
      isbn         = {978-3-95450-247-9},
      pages        = {3575 - 3578},
      year         = {2024},
      note         = {4 pages, 4 figures, 15, 15TH International Particle
                      Accelerator Conference},
      comment      = {[Ebook] 15th International Particle Accelerator Conference,
                      Nashville, Tennessee : May 19-24, 2024, Nashville,
                      Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan
                      , [Geneva] : JACoW Publishing, [2024],},
      booktitle     = {[Ebook] 15th International Particle
                       Accelerator Conference, Nashville,
                       Tennessee : May 19-24, 2024, Nashville,
                       Tennessee, USA : proceedings / Pilat,
                       Fulvia ; Andrian, Ivan , [Geneva] :
                       JACoW Publishing, [2024],},
      abstract     = {High-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.},
      month         = {May},
      date          = {2024-05-19},
      organization  = {The 15th International Particle
                       Accelerator Conference, Nashville
                       (USA), 19 May 2024 - 24 May 2024},
      keywords     = {Accelerator Physics (Other) /
                      mc6-beam-instrumentation-controls-feedback-and-operational-aspects
                      - MC6: Beam Instrumentation, Controls, Feedback, and
                      Operational Aspects (Other) / MC6.T22 - MC6.T22 Reliability,
                      Operability (Other) / klystron (autogen) / operation
                      (autogen) / acceleration (autogen) / timing (autogen) /
                      embedded (autogen)},
      cin          = {MCS 4},
      cid          = {$I:(DE-H253)MCS_4-20120731$},
      pnm          = {6G13 - Accelerator of European XFEL (POF4-6G13)},
      pid          = {G:(DE-HGF)POF4-6G13},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      eprint       = {2405.12391},
      howpublished = {arXiv:2405.12391},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2405.12391;\%\%$},
      doi          = {10.18429/JACoW-IPAC2024-THPR36},
      url          = {https://bib-pubdb1.desy.de/record/607017},
}