JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{radaideh:napac2022-wepa38,
author = {M.I. Radaideh and T.J. Britton and S.M. Cousineau and D. Lu and G.C. Pappas and K. Rajput and M. Schram and L.S. Vidyaratne and J.D. Walden},
% author = {M.I. Radaideh and T.J. Britton and S.M. Cousineau and D. Lu and G.C. Pappas and K. Rajput and others},
% author = {M.I. Radaideh and others},
title = {{Progress on Machine Learning for the SNS High Voltage Converter Modulators}},
& booktitle = {Proc. NAPAC'22},
booktitle = {Proc. 5th Int. Particle Accel. Conf. (NAPAC'22)},
pages = {715--718},
eid = {WEPA38},
language = {english},
keywords = {network, klystron, linac, electron, simulation},
venue = {Albuquerque, NM, USA},
series = {International Particle Accelerator Conference},
number = {5},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {10},
year = {2022},
issn = {2673-7000},
isbn = {978-3-95450-232-5},
doi = {10.18429/JACoW-NAPAC2022-WEPA38},
url = {https://jacow.org/napac2022/papers/wepa38.pdf},
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.}},
}