Author: Piekarski, M.
Paper Title Page
WEPHA121 Deep Neural Network for Anomaly Detection in Accelerators 1375
  • M. Piekarski, W.T. Kitka
    NSRC SOLARIS, Kraków, Poland
  • J. Jaworek-Korjakowska
    AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Kraków, Poland
  The main goal of NSRC SOLARIS is to provide scientific community with high quality synchrotron light. In order to do this it is essential to monitor subsystems that are responsible for beam stability. In this paper a deep neural network for anomaly detection in time series data is proposed. Base model is a pre-trained, 19-layer convolutional neural network VGG-19. Its task is to identify abnormal status of sensors in certain time step. Each time window is a square matrix so can be treated as an image. Any kind of anomalies in synchrotron’s subsystems may lead to beam loss, affect experiments and in extreme cases can cause damage of the infrastructure, therefore when anomaly is detected operator should receive a warning about possible instability.  
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About • paper received ※ 29 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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