IPAC2021 - List of Authors (Shen, G.)

Paper	Title	Page
MOPAB077	Anomaly Detection in Accelerator Facilities Using Machine Learning	304
	A. Das Stanford University, Stanford, California, USA M. Borland, L. Emery, X. Huang, H. Shang, G. Shen ANL, Lemont, Illinois, USA D.F. Ratner SLAC, Menlo Park, California, USA R.M. Smith, G.M. Wang BNL, Upton, New York, USA
	Synchrotron light sources are user facilities and usually run about 5000 hours per year to support many beamlines operations in parallel. Reliability is a key parameter to evaluate machine performance. Even many facilities have achieved >95% beam reliability, there are still many hours of unscheduled downtime and every hour lost is a waste of operation costs along with a big impact on individual scheduled user experiments. Preventive maintenance on subsystems and quick recovery from machine trips are the basic strategies to achieve high reliability, which heavily depends on experts’ dedication. Recently, SLAC, APS, and NSLS-II collaborated to develop machine-learning-based approaches aiming to solve both situations, hardware failure prediction and machine failure diagnosis to find the root sources. In this paper, we report our facility operation status, development progress, and plans.
	Poster MOPAB077 [1.240 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077
About •	paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021
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MOPAB303	Design of the X-Ray Beam Size Monitor for the Advanced Photon Source Upgrade	956
	K.P. Wootton, F.K. Anthony, K. Belcher, J.S. Budz, J. Carwardine, W.X. Cheng, S. Chitra, G. Decker, S.J. Izzo, S.H. Lee, J. Lenner, Z. Liu, P. McNamara, H.V. Nguyen, F.S. Rafael, C. Roehrig, J. Runchey, N. Sereno, G. Shen, J.B. Stevens, B.X. Yang ANL, Lemont, Illinois, USA
	Funding: This research used resources of the Advanced Photon Source, operated for the U.S. Department of Energy Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. A beam size monitor provides an intuitive display of the status of the beam profile and motion in an accelerator. In the present work, we outline the design of the X-ray electron beam size monitor for the Advanced Photon Source Upgrade. Components and anticipated performance characteristics of the beam size monitor are outlined.
	Poster MOPAB303 [0.577 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB303
About •	paper received ※ 18 May 2021 paper accepted ※ 02 June 2021 issue date ※ 24 August 2021
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TUPAB295	Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility	2173
	W. Liu, J.M. Byrd, D.S. Doran, G. Ha, A.N. Johnson, P. Piot, J.G. Power, J.H. Shao, G. Shen, C. Whiteford, E.E. Wisniewski ANL, Lemont, Illinois, USA
	Funding: US Department of Energy, Office of Science The Argonne Wakefield Accelerator (AWA) Test Facility has used a completely homebrewed, MS Windows-based control system for the last 20 years. In an effort to modernize the control system and prepare for an active machine learning program, the AWA will work with the Advanced Photon Source (APS) controls group to upgrade its control system to EPICS. The EPICS control system is expected to facilitate collaborations and support the future growth of AWA. An overview of the previous AWA control and data acquisition system is presented, along with a vision and path for completing the EPICS upgrade.
	Poster TUPAB295 [1.108 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB295
About •	paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 30 August 2021
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WEPAB323	High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade	3434
	G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli ANL, Lemont, Illinois, USA
	Funding: Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357. It is well known that the efficiency of an advanced control algorithm like machine learning is as good as its data quality. Much recent progress in technology enables the massive data acquisition from a control system of modern particle accelerator, and the wide use of embedded controllers, like field-programmable gate arrays (FPGA), provides an opportunity to collect fast data from technical subsystems for monitoring, statistics, diagnostics or fault recording. To improve the data quality, at the APS Upgrade project, a general-purpose data acquisition (DAQ) system is under active development. The APS-U DAQ system collects high-quality fast data from underneath embedded controllers, especially the FPGAs, with the manner of time-correlation and synchronously sampling, which could be used for commissioning, performance monitoring, troubleshooting, and early fault detection, etc. This paper presents the design and latest progress of APS-U high-performance DAQ infrastructure, as well as its preparation to enable the use of machine learning technology for APS-U, and its use cases at APS.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323
About •	paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 29 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Anomaly Detection in Accelerator Facilities Using Machine Learning

304

A. Das
Stanford University, Stanford, California, USA
M. Borland, L. Emery, X. Huang, H. Shang, G. Shen
ANL, Lemont, Illinois, USA
D.F. Ratner
SLAC, Menlo Park, California, USA
R.M. Smith, G.M. Wang
BNL, Upton, New York, USA

Synchrotron light sources are user facilities and usually run about 5000 hours per year to support many beamlines operations in parallel. Reliability is a key parameter to evaluate machine performance. Even many facilities have achieved >95% beam reliability, there are still many hours of unscheduled downtime and every hour lost is a waste of operation costs along with a big impact on individual scheduled user experiments. Preventive maintenance on subsystems and quick recovery from machine trips are the basic strategies to achieve high reliability, which heavily depends on experts’ dedication. Recently, SLAC, APS, and NSLS-II collaborated to develop machine-learning-based approaches aiming to solve both situations, hardware failure prediction and machine failure diagnosis to find the root sources. In this paper, we report our facility operation status, development progress, and plans.

Poster MOPAB077 [1.240 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077

About •

paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021

Export •

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

MOPAB303

Design of the X-Ray Beam Size Monitor for the Advanced Photon Source Upgrade

956

K.P. Wootton, F.K. Anthony, K. Belcher, J.S. Budz, J. Carwardine, W.X. Cheng, S. Chitra, G. Decker, S.J. Izzo, S.H. Lee, J. Lenner, Z. Liu, P. McNamara, H.V. Nguyen, F.S. Rafael, C. Roehrig, J. Runchey, N. Sereno, G. Shen, J.B. Stevens, B.X. Yang
ANL, Lemont, Illinois, USA

Funding: This research used resources of the Advanced Photon Source, operated for the U.S. Department of Energy Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
A beam size monitor provides an intuitive display of the status of the beam profile and motion in an accelerator. In the present work, we outline the design of the X-ray electron beam size monitor for the Advanced Photon Source Upgrade. Components and anticipated performance characteristics of the beam size monitor are outlined.

Poster MOPAB303 [0.577 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB303

About •

paper received ※ 18 May 2021 paper accepted ※ 02 June 2021 issue date ※ 24 August 2021

Export •

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

TUPAB295

Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility

2173

W. Liu, J.M. Byrd, D.S. Doran, G. Ha, A.N. Johnson, P. Piot, J.G. Power, J.H. Shao, G. Shen, C. Whiteford, E.E. Wisniewski
ANL, Lemont, Illinois, USA

Funding: US Department of Energy, Office of Science
The Argonne Wakefield Accelerator (AWA) Test Facility has used a completely homebrewed, MS Windows-based control system for the last 20 years. In an effort to modernize the control system and prepare for an active machine learning program, the AWA will work with the Advanced Photon Source (APS) controls group to upgrade its control system to EPICS. The EPICS control system is expected to facilitate collaborations and support the future growth of AWA. An overview of the previous AWA control and data acquisition system is presented, along with a vision and path for completing the EPICS upgrade.

Poster TUPAB295 [1.108 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB295

About •

paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 30 August 2021

Export •

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

WEPAB323

High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade

3434

G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli
ANL, Lemont, Illinois, USA

Funding: Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357.
It is well known that the efficiency of an advanced control algorithm like machine learning is as good as its data quality. Much recent progress in technology enables the massive data acquisition from a control system of modern particle accelerator, and the wide use of embedded controllers, like field-programmable gate arrays (FPGA), provides an opportunity to collect fast data from technical subsystems for monitoring, statistics, diagnostics or fault recording. To improve the data quality, at the APS Upgrade project, a general-purpose data acquisition (DAQ) system is under active development. The APS-U DAQ system collects high-quality fast data from underneath embedded controllers, especially the FPGAs, with the manner of time-correlation and synchronously sampling, which could be used for commissioning, performance monitoring, troubleshooting, and early fault detection, etc. This paper presents the design and latest progress of APS-U high-performance DAQ infrastructure, as well as its preparation to enable the use of machine learning technology for APS-U, and its use cases at APS.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323

About •

paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 29 August 2021

Export •

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