1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Enhancing quench detection in SRF cavities at the EuXFEL: Towards machine learning approaches and practical challenges

THPS134
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Salmon (TWTC)

Exhibiton Hall A _Salmon

TWTC

Poster Presentation MC6.T27 Low Level RF Thursday Poster Session

Speaker

Nadeem Shehzad (Deutsches Elektronen-Synchrotron DESY)

Description

Detecting anomalies in superconducting cavities at the EuXFEL is essential for reliable operation. We began with a model-based anomaly detection approach focused on residual analysis. To improve fault discrimination, particularly for quench events, we augmented the detection with a machine learning-based classification. Key challenges are posed by the transition to real-time operation, requiring computational and integration adjustments. For the online application, we deployed two servers at one of the 25 stations to detect and log anomalies with a software implementation. In parallel, we pushed the development of a firmware solution that will counteract critical faults in real-time. At the current stage only the anomaly detection is in online operation, which is planned to be augmented with the online fault classification in the future. The resulting detection system delivers reports across various timescales, supporting both immediate responses and long-term maintenance.

Region represented Europe
Paper preparation format LaTeX

Author

Annika Eichler (Deutsches Elektronen-Synchrotron DESY)

Co-authors

Bozo Richter (Deutsches Elektronen-Synchrotron DESY) Burak Dursun (Deutsches Elektronen-Synchrotron DESY) Julien Branlard (Deutsches Elektronen-Synchrotron DESY) Dr Lynda Boukela (Deutsches Elektronen-Synchrotron DESY) Marco Diomede (Deutsches Elektronen-Synchrotron DESY) Nadeem Shehzad (Deutsches Elektronen-Synchrotron DESY)

Presentation materials

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