JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Pollard, A.E. AU - Dunning, D.J. AU - Gilfellon, A.J. ED - McIntosh, Peter ED - Burt, Graeme ED - Apsimon, Robert ED - Schaa, Volker R.W. TI - Machine Learning for RF Breakdown Detection at CLARA J2 - Proc. of LINAC2022, Liverpool, UK, 28 August-02 September 2022 CY - Liverpool, UK T2 - International Linear Accelerator Conference T3 - 31 LA - english AB - Maximising the accelerating gradient of RF structures is fundamental to improving accelerator facility performance and cost-effectiveness. Structures must be subjected to a conditioning process before operational use, in which the gradient is gradually increased up to the operating value. A limiting effect during this process is breakdown or vacuum arcing, which can cause damage that limits the ultimate operating gradient. Techniques to efficiently condition the cavities while minimising the number of breakdowns are therefore important. In this paper, machine learning techniques are applied to detect breakdown events in RF pulse traces by approaching the problem as anomaly detection, using a variational autoencoder. This process detects deviations from normal operation and classifies them with near perfect accuracy. Offline data from various sources has been used to develop the techniques, which we aim to test at the CLARA facility at Daresbury Laboratory. Deployment of the machine learning system on the high repetition rate gun upgrade at CLARA has begun. PB - JACoW Publishing CP - Geneva, Switzerland SP - 858 EP - 862 KW - cavity KW - network KW - detector KW - gun KW - operation DA - 2022/09 PY - 2022 SN - 2226-0366 SN - 978-3-95450-215-8 DO - doi:10.18429/JACoW-LINAC2022-THPORI16 UR - https://jacow.org/linac2022/papers/thpori16.pdf ER -