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A data-driven anomaly detection on SRF cavities at the European XFEL
Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
Author(s)
First published in
Number in series
2420
Volume
2420
Issue
1
Article Number
012070
Citation
Journal of Physics: Conference Series, Volume 2420, 13th International Particle Accelerator Conference (IPAC'22) 12 - 17 June 2022, Bangkok, Thailand. - 012070 (2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IOP Publ.
The European XFEL is currently operating with hundreds of superconducting radio frequency cavities. To be able to minimize the downtimes, prevention of failures on the SRF cavities is crucial. In this paper, we propose an anomaly detection approach based on a neural network model to predict occurrences of breakdowns on the SRF cavities based on a model trained on historical data. We used our existing anomaly detection infrastructure to get a subset of the stored data labeled as faulty. We experimented with different training losses to maximally profit from the available data and trained a recurrent neural network that can predict a failure from a series of pulses. The proposed model is using a tailored architecture with recurrent neural units and takes into account the sequential nature of the problem which can generalize and predict a variety of failures that we have been experiencing in operation.
Subjects
MLE@TUHH
DDC Class
621.3: Electrical Engineering, Electronic Engineering