IPAC'25 - the 16th International Particle Accelerator Conferece

Enhancing quench detection in SRF cavities at the European XFEL: machine learning approaches and practical challenges

THPS134

5 Jun 2025, 15:30

2h

Exhibiton Hall A _Salmon (TWTC)

Poster Presentation MC6.T27 Low Level RF Thursday Poster Session

Speaker

Annika Eichler (Deutsches Elektronen-Synchrotron DESY)

Description

At the European XFEL, detecting anomalies in superconducting cavities is essential for reliable accelerator performance. We began with a model-based fault detection approach focused on residual analysis to identify anomalies. To improve fault discrimination, particularly for quench events, we augmented this system with machine learning (ML) models. Key challenges included the scarcity of labeled data, which we addressed by integrating expert feedback through an optimized process, and the transition to real-time operation, requiring computational and integration adjustments. The resulting detection system delivers reports across various timescales, supporting both immediate responses and long-term maintenance. For the online application, we deployed two servers in the tunnel at one of the 25 stations to detect failures in real-time with a software-based solution. In parallel, we developed an FPGA-based solution to provide real-time fault counteraction in the near future. It will provide new insights to the online data, which was never explored in the past.