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Machine learning assisted cavity quench identification at the European XFEL
Publikationstyp
Conference Paper
Date Issued
2022
Sprache
English
Author(s)
Start Page
799
End Page
802
Citation
Proceedings - International Linear Accelerator Conference, LINAC 2022, Liverpool, 28 August-2 September 2022. - Seite 799-802 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
JACoW Publishing
ISBN
978-3-95450-215-8
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 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.
Subjects
MLE@TUHH
DDC Class
621.3: Electrical Engineering, Electronic Engineering