Branlard, JulienJulienBranlardEichler, AnnikaAnnikaEichlerTimm, J.J.TimmWalker, N.N.Walker2024-06-182024-06-182022Proceedings - International Linear Accelerator Conference, LINAC 2022, Liverpool, 28 August-2 September 2022. - Seite 799-802 (2022)978-3-95450-215-8https://hdl.handle.net/11420/47908A 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 station 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 subset of such residuals previously tagged as “quench”/“non-quench”, two independent machine learning engines analyse 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.enMLE@TUHHTechnology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringConference Paper10.18429/JACoW-LINAC2022-THPOPA26Conference Paper