Author: Britton, T.J.
Paper Title Page
WEPA38 Progress on Machine Learning for the SNS High Voltage Converter Modulators 715
  • M.I. Radaideh, S.M. Cousineau, D. Lu
    ORNL, Oak Ridge, Tennessee, USA
  • T.J. Britton, K. Rajput, M. Schram, L.S. Vidyaratne
    JLab, Newport News, Virginia, USA
  • G.C. Pappas, J.D. Walden
    ORNL RAD, Oak Ridge, Tennessee, USA
  The High-Voltage Converter Modulators (HVCM) used to power the klystrons in the Spallation Neutron Source (SNS) linac were selected as one area to explore machine learning due to reliability issues in the past and the availability of large sets of archived waveforms. Progress in the past two years has resulted in generating a significant amount of simulated and measured data for training neural network models such as recurrent neural networks, convolutional neural networks, and variational autoencoders. Applications in anomaly detection, fault classification, and prognostics of capacitor degradation were pursued in collaboration with the Jefferson Laboratory, and early promising results were achieved. This paper will discuss the progress to date and present results from these efforts.  
poster icon Poster WEPA38 [1.320 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA38  
About • Received ※ 25 July 2022 — Revised ※ 08 August 2022 — Accepted ※ 11 August 2022 — Issue date ※ 03 October 2022
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