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TY - CONF AU - Peters, C.C. AU - Blokland, W. AU - Brown, D.L. AU - Liu, F. AU - Long, C.D. AU - Lu, D. AU - Ramuhalli, P. AU - Womble, D.E. AU - Zhang, J. AU - Zhukov, A.P. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Machine Learning for Time Series Prediction of an Accelerator Beam to Recognize Equipment Malfunction J2 - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021 CY - Campinas, SP, Brazil T2 - International Particle Accelerator Conference T3 - 12 LA - english AB - The Spallation Neutron Source (SNS) is an accelerator based pulsed neutron source based on a 1 GeV pulsed proton Superconducting Radio Frequency (SRF) linear accelerator (linac). Since beginning high power beam operation in 2006 correlations have been found linking abrupt beam loss events to SRF cavity instabilities. With the planned upgrades to double the beam power we expect increased rates of degradation and the importance of minimizing these beam loss events will become ever more important. To further limit degradation, we are developing machine learning approaches to monitor the beam and to detect, predict and prevent beam loss events. Initial research has shown that precursors to beam loss events are detectable. The initial steps are to use ML-based classification to recognize anomalies and to use Long Short-Term Memory (LSTM) autoencoders to predict beam loss. In this paper, we describe recent progress in applying machine learning for recognizing anomalies and predicting beam loss and present initial results of our research using acquired data from different diagnostics and the Machine Protection System (MPS). PB - JACoW Publishing CP - Geneva, Switzerland SP - 2272 EP - 2275 KW - cavity KW - SRF KW - linac KW - ion-source KW - neutron DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-TUPAB328 UR - https://jacow.org/ipac2021/papers/tupab328.pdf ER -