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Title Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators
  • G.C. Pappas
    ORNL RAD, Oak Ridge, Tennessee, USA
  • D. Lu
    ORNL, Oak Ridge, Tennessee, USA
  • M. Schram
    JLab, Newport News, Virginia, USA
  • D.L. Vrabie
    PNNL, Richland, Washington, USA
Abstract Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
Footnotes & References * G. Dodson, Approach to Reliable Operations, 26-DodsonApproach to Reliable Operation-r1.pdf, Feb., 2010.
Funding SNS/ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 for the U.S. Department of Energy
Paper download THPAB252.PDF [2.240 MB / 4 pages]
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Conference IPAC2021
Series International Particle Accelerator Conference (12th)
Location Campinas, SP, Brazil
Date 24-28 May 2021
Publisher JACoW Publishing, Geneva, Switzerland
Editorial Board Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany)
Online ISBN 978-3-95450-214-1
Online ISSN 2673-5490
Received 18 May 2021
Accepted 14 July 2021
Issue Date 29 August 2021
DOI doi:10.18429/JACoW-IPAC2021-THPAB252
Pages 4303-4306
Creative Commons CC logoPublished by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.