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

Autor: Sichen Li, Mélissa Zacharias, Jochem Snuverink, Jaime Coello de Portugal, Fernando Perez-Cruz, Davide Reggiani, Andreas Adelmann
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Information, Vol 12, Iss 3, p 121 (2021)
Druh dokumentu: article
ISSN: 2078-2489
DOI: 10.3390/info12030121
Popis: 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.
Databáze: Directory of Open Access Journals