LINAC2022 - List of Keywords (hardware)

Paper	Title	Other Keywords	Page
MOPORI17	The ESS Fast Beam Interlock System: First Experience of Operating With Proton Beam	MMI, interface, controls, proton	265
	S. Gabourin, M. Carroll, S. Kövecses de Carvalho, A. Nordt, S. Pavinato, K. Rosquist ESS, Lund, Sweden
	The European Spallation Source (ESS), Sweden, currently in its early operation phase, aims to be the most powerful neutron source in the world. Proton beam pulses are accelerated and sent to a rotating tungsten target, where neutrons are generated via the spallation effect. The damage potential of the ESS proton beam is high and melting of copper or steel can happen within less than 5 microseconds. Therefore, highly reliable and fast machine protection (MP) systems have been designed and deployed. The core system of ESS Machine Protection is the Fast Beam Interlock System (FBIS), based on FPGA technology. FBIS collects data from all relevant accelerator and target systems through 300 direct inputs and decides whether beam operation can start or must stop. The architecture is based on two main building blocks: Decision Logic Node (DLN), executing the protection logic and realizing interfaces to Higher-Level Safety, Timing System and EPICS Control System. The second block, the Signal Condition Unit (SCU), implements the interface between FBIS inputs/outputs and DLNs. This paper gives an overview on FBIS and a summary on its performance during beam commissioning phases since 2021.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPORI17
About •	Received ※ 19 August 2022 — Revised ※ 26 August 2022 — Accepted ※ 02 September 2022 — Issue date ※ 03 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOPA12	Development and Integration of a New Low-Level RF System for MedAustron	cavity, controls, LLRF, synchrotron	764
	M. Wolf, M. Cerv, C. Kurfürst, G. Muyan, S. Myalski, M. Repovž, C. Schmitzer EBG MedAustron, Wr. Neustadt, Austria A. Bardorfer, B. Baričevič, P. Paglovec, M. Škabar I-Tech, Solkan, Slovenia
	The MedAustron Ion Therapy Centre is a synchrotron-based particle therapy facility, which delivers proton and carbon beams for clinical treatments. Currently, the facility treats 40 patients per day and is improving its systems and workflows to further increase this number. Although MedAustron is a young and modern center, the life-cycle of certain crucial control electronics is near end-of-life and needs to be addressed. This paper presents the 216MHz injector Low-Level Radio Frequency (iLLRF) system with option of use for the synchrotron Low-Level Radio Frequency (sLLRF - 0.4-10MHz). The developed system will unify the cavity regulation for both LLRFs and will also be used for beam diagnostics (injector/synchrotron) and RF knock-out slow extraction. The new LLRF system is based on a µTCA platform which is controlled by the MedAustron Control System based on NI-PXIe. Currently, it supports fiberoptics links (SFP+), but other links (e.g. EPICS, DOOCS) can be established. The modular implementation of this LLRF allows connections to other components, such as motors, amplifiers, or interlock systems, and will increase the robustness and maintainability of the accelerator.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA12
About •	Received ※ 24 August 2022 — Revised ※ 25 August 2022 — Accepted ※ 31 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPOPA26	Machine Learning Assisted Cavity Quench Identification at the European XFEL	cavity, FEL, operation, software	798
	J. Branlard, A. Eichler, J.H.K. Timm, N. Walker DESY, Hamburg, Germany
	A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a set of such residuals derived from quench as well as non-quench events, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.
	Slides THPOPA26 [0.695 MB]
	Poster THPOPA26 [0.975 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA26
About •	Received ※ 19 August 2022 — Revised ※ 24 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPORI17

The ESS Fast Beam Interlock System: First Experience of Operating With Proton Beam

MMI, interface, controls, proton

265

S. Gabourin, M. Carroll, S. Kövecses de Carvalho, A. Nordt, S. Pavinato, K. Rosquist
ESS, Lund, Sweden

The European Spallation Source (ESS), Sweden, currently in its early operation phase, aims to be the most powerful neutron source in the world. Proton beam pulses are accelerated and sent to a rotating tungsten target, where neutrons are generated via the spallation effect. The damage potential of the ESS proton beam is high and melting of copper or steel can happen within less than 5 microseconds. Therefore, highly reliable and fast machine protection (MP) systems have been designed and deployed. The core system of ESS Machine Protection is the Fast Beam Interlock System (FBIS), based on FPGA technology. FBIS collects data from all relevant accelerator and target systems through 300 direct inputs and decides whether beam operation can start or must stop. The architecture is based on two main building blocks: Decision Logic Node (DLN), executing the protection logic and realizing interfaces to Higher-Level Safety, Timing System and EPICS Control System. The second block, the Signal Condition Unit (SCU), implements the interface between FBIS inputs/outputs and DLNs. This paper gives an overview on FBIS and a summary on its performance during beam commissioning phases since 2021.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPORI17

About •

Received ※ 19 August 2022 — Revised ※ 26 August 2022 — Accepted ※ 02 September 2022 — Issue date ※ 03 September 2022

Cite •

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

THPOPA12

Development and Integration of a New Low-Level RF System for MedAustron

cavity, controls, LLRF, synchrotron

764

M. Wolf, M. Cerv, C. Kurfürst, G. Muyan, S. Myalski, M. Repovž, C. Schmitzer
EBG MedAustron, Wr. Neustadt, Austria
A. Bardorfer, B. Baričevič, P. Paglovec, M. Škabar
I-Tech, Solkan, Slovenia

The MedAustron Ion Therapy Centre is a synchrotron-based particle therapy facility, which delivers proton and carbon beams for clinical treatments. Currently, the facility treats 40 patients per day and is improving its systems and workflows to further increase this number. Although MedAustron is a young and modern center, the life-cycle of certain crucial control electronics is near end-of-life and needs to be addressed. This paper presents the 216MHz injector Low-Level Radio Frequency (iLLRF) system with option of use for the synchrotron Low-Level Radio Frequency (sLLRF - 0.4-10MHz). The developed system will unify the cavity regulation for both LLRFs and will also be used for beam diagnostics (injector/synchrotron) and RF knock-out slow extraction. The new LLRF system is based on a µTCA platform which is controlled by the MedAustron Control System based on NI-PXIe. Currently, it supports fiberoptics links (SFP+), but other links (e.g. EPICS, DOOCS) can be established. The modular implementation of this LLRF allows connections to other components, such as motors, amplifiers, or interlock systems, and will increase the robustness and maintainability of the accelerator.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA12

About •

Received ※ 24 August 2022 — Revised ※ 25 August 2022 — Accepted ※ 31 August 2022 — Issue date ※ 01 September 2022

Cite •

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

THPOPA26

Machine Learning Assisted Cavity Quench Identification at the European XFEL

cavity, FEL, operation, software

798

J. Branlard, A. Eichler, J.H.K. Timm, N. Walker
DESY, Hamburg, Germany

A server-based quench detection system is used since the beginning of operation at the European XFEL (2017) to stop driving superconducting cavities if they experience a quench. While this approach effectively detects quenches, it also generates false positives, tripping the accelerating stations when failures other than quenches occur. Using the post-mortem data snapshots generated for every trip, an additional signal (referred to as residual) is systematically computed based on the standard cavity model. Following an initial training on a set of such residuals derived from quench as well as non-quench events, two independent machine learning engines analyze routinely the trip snapshots and their residuals to identify if a trip was indeed triggered by a quench or has another root cause. The outcome of the analysis is automatically appended to the data snapshots and distributed to a team of experts. This constitutes a fully deployed example of machine-learning-assisted failure classification to identify quenches, supporting experts in their daily routine of monitoring and documenting the accelerator uptime and availability.

Slides THPOPA26 [0.695 MB]

Poster THPOPA26 [0.975 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOPA26

About •

Received ※ 19 August 2022 — Revised ※ 24 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 01 September 2022

Cite •

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