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 - Rahman, M. AU - Carpenter, A. AU - Iftekharuddin, K.M. AU - McGuckin, T.S. AU - Tennant, C. AU - Vidyaratne, L.S. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types. PB - JACoW Publishing CP - Geneva, Switzerland SP - 687 EP - 690 KW - cavity KW - network KW - cryomodule KW - SRF KW - experiment DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-WEPA29 UR - https://jacow.org/napac2022/papers/wepa29.pdf ER -