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@INPROCEEDINGS{Eichler:632237,
      author       = {Eichler, Annika and Shehzad, Nadeem and Branlard, Julien
                      and Boukela, Lynda and Dursun, Burak and Diomede, Marco and
                      Richter, Bozo},
      title        = {{ENHANCING} {QUENCH} {DETECTION} {IN} {SRF} {CAVITIES} {AT}
                      {THE} {EUXFEL}:{TOWARDS} {MACHINE} {LEARNING} {APPROACHES}
                      {ANDPRACTICAL} {CHALLENGES}},
      address      = {Geneva},
      publisher    = {JACoW},
      reportid     = {PUBDB-2025-02163},
      isbn         = {978-3-95450-248-6},
      pages        = {4},
      year         = {2025},
      abstract     = {Detecting anomalies in superconducting cavities at the
                      EuXFEL is essential for reliable operation. We began with a
                      model-based anomaly detection approach focused on residual
                      analysis. To improve fault discrimination, particularly for
                      quench events, we augmented the detection with a machine
                      learning-based classification. Key challenges are posed by
                      the transition to real-time operation, requiring
                      computational and integration adjustments. For the online
                      application, we deployed two servers at one of the 25
                      stations to detect and log anomalies with a software
                      implementation. In parallel, we pushed the development of a
                      firmware solution that will counteract critical faults in
                      real-time. At the current stage only the anomaly detection
                      is in online operation, which is planned to be augmented
                      with the online fault classification in the future. The
                      resulting detection system delivers reports across various
                      timescales, supporting both immediate responses and
                      long-term maintenance.},
      month         = {Jun},
      date          = {2025-06-01},
      organization  = {16th International Particle
                       Accelerator Conference, Taipei
                       (Taiwan), 1 Jun 2025 - 6 Jun 2025},
      cin          = {MSK},
      cid          = {I:(DE-H253)MSK-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) / 623
                      - Data Management and Analysis (POF4-623)},
      pid          = {G:(DE-HGF)POF4-621 / G:(DE-HGF)POF4-623},
      experiment   = {EXP:(DE-H253)XFEL-Exp-20150101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACoW-IPAC25-THPS134},
      url          = {https://bib-pubdb1.desy.de/record/632237},
}