JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Branlard, J. AU - Eichler, A. AU - Timm, J.H.K. AU - Walker, N. ED - McIntosh, Peter ED - Burt, Graeme ED - Apsimon, Robert ED - Schaa, Volker R.W. TI - Machine Learning Assisted Cavity Quench Identification at the European XFEL J2 - Proc. of LINAC2022, Liverpool, UK, 28 August-02 September 2022 CY - Liverpool, UK T2 - International Linear Accelerator Conference T3 - 31 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 798 EP - 801 KW - cavity KW - FEL KW - operation KW - software KW - hardware DA - 2022/09 PY - 2022 SN - 2226-0366 SN - 978-3-95450-215-8 DO - doi:10.18429/JACoW-LINAC2022-THPOPA26 UR - https://jacow.org/linac2022/papers/thpopa26.pdf ER -