Author: Timm, J.H.K.
Paper Title Page
THPOPA26 Machine Learning Assisted Cavity Quench Identification at the European XFEL 798
THOPA09   use link to see paper's listing under its alternate paper code  
 
  • J. Branlard, A. Eichler, J.H.K. Timm, N. Walker
    DESY, Hamburg, Germany
 
  A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a set of such residuals derived from quench as well as non-quench events, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.  
slides icon Slides THPOPA26 [0.695 MB]  
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DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA26  
About • Received ※ 19 August 2022 — Revised ※ 24 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 01 September 2022
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