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Article

A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators

1
Paul Scherrer Institut, 5232 Villigen, Switzerland
2
Swiss Data Science Center, ETH Zürich and EPFL, Universitätstrasse 25, 8092 Zürich, Switzerland
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Giorgio Kaniadakis
Information 2021, 12(3), 121; https://doi.org/10.3390/info12030121
Received: 5 February 2021 / Revised: 5 March 2021 / Accepted: 10 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue Machine Learning and Accelerator Technology)
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss of beam time. A novel time series classification approach is applied to decrease beam time loss in the High-Intensity Proton Accelerator complex by forecasting interlock events. The forecasting is performed through binary classification of windows of multivariate time series. The time series are transformed into Recurrence Plots which are then classified by a Convolutional Neural Network, which not only captures the inner structure of the time series but also uses the advances of image classification techniques. Our best-performing interlock-to-stable classifier reaches an Area under the ROC Curve value of 0.71±0.01 compared to 0.65±0.01 of a Random Forest model, and it can potentially reduce the beam time loss by 0.5±0.2 s per interlock. View Full-Text
Keywords: time series classification; recurrence plot; convolutional neural network; random forest; charged particle accelerator time series classification; recurrence plot; convolutional neural network; random forest; charged particle accelerator
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MDPI and ACS Style

Li, S.; Zacharias, M.; Snuverink, J.; Coello de Portugal, J.; Perez-Cruz, F.; Reggiani, D.; Adelmann, A. A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators. Information 2021, 12, 121. https://doi.org/10.3390/info12030121

AMA Style

Li S, Zacharias M, Snuverink J, Coello de Portugal J, Perez-Cruz F, Reggiani D, Adelmann A. A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators. Information. 2021; 12(3):121. https://doi.org/10.3390/info12030121

Chicago/Turabian Style

Li, Sichen, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, and Andreas Adelmann. 2021. "A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators" Information 12, no. 3: 121. https://doi.org/10.3390/info12030121

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