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RIS citation export for WEPA29: Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

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  -