NAPAC2022 - List of Authors (Walden, J.D.)

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 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
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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 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

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)