Author: McGuckin, T.S.
Paper Title Page
WEPA29 Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning 687
 
  • M. Rahman, K.M. Iftekharuddin
    ODU, Norfolk, Virginia, USA
  • A. Carpenter, T.S. McGuckin, C. Tennant, L.S. Vidyaratne
    JLab, Newport News, Virginia, USA
 
  Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177.
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.
 
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DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA29  
About • Received ※ 02 August 2022 — Revised ※ 07 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 17 August 2022
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