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

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 \pm 0.01

compared to

0.65 \pm 0.01

of a Random Forest model, and it can potentially reduce the beam time loss by

0.5 \pm 0.2

s per interlock. View Full-Text

Keywords: 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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