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Title Progress on Machine Learning for the SNS High Voltage Converter Modulators
  • 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
Abstract 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.
Paper download WEPA38.PDF [2.032 MB / 4 pages]
Poster download WEPA38_POSTER.PDF [1.385 MB]
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Conference NAPAC2022
Series International Particle Accelerator Conference (5th)
Location Albuquerque, NM, USA
Date 07-12 August 2022
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Sandra Biedron (Element Aero, Chicago, IL, USA); Evgenya Simakov (LANL, Los Alamos, NM, USA); Stephen Milton (Element Aero, Chicago, IL, USA); Petr M. Anisimov (LANL, Los Alamos, NM, USA); Volker R.W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-232-5
Online ISSN 2673-7000
Received 25 July 2022
Revised 08 August 2022
Accepted 11 August 2022
Issue Date 03 October 2022
DOI doi:10.18429/JACoW-NAPAC2022-WEPA38
Pages 715-718
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.