19–24 May 2024
Music City Center
US/Central timezone

Automated anomaly detection on European XFEL klystrons

THPR36
23 May 2024, 16:00
2h
Rock 'n Roll (MCC Exhibit Hall A)

Rock 'n Roll

MCC Exhibit Hall A

Poster Presentation MC6.T22 Reliability, Operability Thursday Poster Session

Speaker

Antonin Sulc (Helmholtz-Zentrum Berlin fuer Materialien und Energie GmbH)

Description

High-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL. Exchanging these high-power components takes time and effort, thus it is necessary to minimize maintenance and downtime and at the same time maximize the device's operation. In an attempt to explore the behavior of klystrons using machine learning, we completed a series of experiments on our klystrons to determine various operational modes and conduct feature extraction and dimensionality reduction to extract the most valuable information about a normal operation. To analyze recorded data we used state-of-the-art data-driven learning techniques and recognized the most promising components that might help us better understand klystron operational states and identify early on possible faults or anomalies.

Region represented Europe
Paper preparation format LaTeX

Primary author

Antonin Sulc (Helmholtz-Zentrum Berlin fuer Materialien und Energie GmbH)

Co-authors

Annika Eichler (Deutsches Elektronen-Synchrotron) Tim Wilksen (Deutsches Elektronen-Synchrotron)

Presentation materials

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