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RIS citation export for WEPA38: Progress on Machine Learning for the SNS High Voltage Converter Modulators

AU  - Radaideh, M.I.
AU  - Britton, T.J.
AU  - Cousineau, S.M.
AU  - Lu, D.
AU  - Pappas, G.C.
AU  - Rajput, K.
AU  - Schram, M.
AU  - Vidyaratne, L.S.
AU  - Walden, J.D.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Progress on Machine Learning for the SNS High Voltage Converter Modulators
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - 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.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 715
EP  - 718
KW  - network
KW  - klystron
KW  - linac
KW  - electron
KW  - simulation
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-WEPA38
UR  - https://jacow.org/napac2022/papers/wepa38.pdf
ER  -