ICALEPCS2019 - List of Authors (Kitka, W.T.)

Paper	Title	Page
MOPHA072	Automation in NSRC SOLARIS With Python and Tango Controls	382
	W.T. Kitka, M.K. Falowski, A.M. Marendziak, N. Olszowska, M. Zając NSRC SOLARIS, Kraków, Poland
	NSRC SOLARIS is a 1.5 GeV third generation light source constructed at Jagiellonian University in Kraków, Poland. The machine was commissioned in April 2016 and operates in decay mode. Two beamlines PEEM/XAS and UARPES were commissioned in 2018 and they have opened for conducting research in fall 2018. Two more beamlines (PHELIX and XMCD) are installed now and will be commissioned soon. Due to small size of the team and many concurrent tasks, automation is very important. Automating many tasks in a quick and effective way is possible thanks to the control system based on TANGO Controls and Python programming language. With facadevice library the necessary values can be easily calculated in real-time. Beam position correction with PID controller at PEEM/XAS and UARPES beamlines, alarm handling in SOLARIS Heating Unit Controller and real-time calculation of various vacuum parameters are shown as examples.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA072
About •	paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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.
	Poster WEPHA121 [1.368 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA121
About •	paper received ※ 29 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Automation in NSRC SOLARIS With Python and Tango Controls

382

W.T. Kitka, M.K. Falowski, A.M. Marendziak, N. Olszowska, M. Zając
NSRC SOLARIS, Kraków, Poland

NSRC SOLARIS is a 1.5 GeV third generation light source constructed at Jagiellonian University in Kraków, Poland. The machine was commissioned in April 2016 and operates in decay mode. Two beamlines PEEM/XAS and UARPES were commissioned in 2018 and they have opened for conducting research in fall 2018. Two more beamlines (PHELIX and XMCD) are installed now and will be commissioned soon. Due to small size of the team and many concurrent tasks, automation is very important. Automating many tasks in a quick and effective way is possible thanks to the control system based on TANGO Controls and Python programming language. With facadevice library the necessary values can be easily calculated in real-time. Beam position correction with PID controller at PEEM/XAS and UARPES beamlines, alarm handling in SOLARIS Heating Unit Controller and real-time calculation of various vacuum parameters are shown as examples.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA072

About •

paper received ※ 30 September 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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.

Poster WEPHA121 [1.368 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA121

About •

paper received ※ 29 September 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)