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URLhttps://doi.org/10.18429/JACoW-IPAC25-THPS134
TitleEnhancing quench detection in SRF cavities at the EuXFEL: Towards machine learning approaches and practical challenges
Authors
  • A. Eichler, B. Richter, B. Dursun, J. Branlard, L. Boukela, M. Diomede, N. Shehzad
    Deutsches Elektronen-Synchrotron DESY
AbstractDetecting 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.
Paperdownload: THPS134.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference16th International Particle Accelerator Conference
Series
LocationTaipei, Taiwan
Date01-06 Jun 2025
PublisherJACoW Publishing, Geneva, Switzerland
Editorial Board* Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center
Online ISBN978-3-95450-248-6
Online ISSN2673-5490
Received28 May 2025
Revised01 June 2025
Accepted05 June 2025
Issued10 July 2025
DOI10.18429/JACoW-IPAC25-THPS134
Pages3226-3229