| Title |
Deep Neural Network for Anomaly Detection in Accelerators |
| Authors |
- 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
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| Abstract |
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.
|
| Paper |
download WEPHA121.PDF [3.033 MB / 4 pages] |
| Poster |
download WEPHA121_POSTER.PDF [1.368 MB] |
| Export |
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| Conference |
ICALEPCS2019 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (17th) |
| Location |
New York, NY, USA |
| Date |
05-11 October 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-209-7 |
| Online ISSN |
2226-0358 |
| Received |
29 September 2019 |
| Accepted |
09 October 2019 |
| Issue Date |
30 August 2020 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2019-WEPHA121 |
| Pages |
1375-1378 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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