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TY - CONF AU - Piekarski, M. AU - Jaworek-Korjakowska, J. AU - Kitka, W.T. ED - White, Karen S. ED - Brown, Kevin A. ED - Dyer, Philip S. ED - Schaa, Volker RW TI - Deep Neural Network for Anomaly Detection in Accelerators J2 - Proc. of ICALEPCS2019, New York, NY, USA, 05-11 October 2019 CY - New York, NY, USA T2 - International Conference on Accelerator and Large Experimental Physics Control Systems T3 - 17 LA - english AB - 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. PB - JACoW Publishing CP - Geneva, Switzerland SP - 1375 EP - 1378 KW - network KW - synchrotron KW - Windows KW - operation KW - controls DA - 2020/08 PY - 2020 SN - 2226-0358 SN - 978-3-95450-209-7 DO - doi:10.18429/JACoW-ICALEPCS2019-WEPHA121 UR - https://jacow.org/icalepcs2019/papers/wepha121.pdf ER -