ICALEPCS2019 - List of Keywords (kicker)

Paper	Title	Other Keywords	Page
MOPHA068	Improving Reliability of the Fast Extraction Kicker Timing Control at the AGS	software, timing, extraction, controls	373
	P.K. Kankiya, J.P. Jamilkowski BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The fast extraction kicker system at AGS to RHIC transport line uses Stanford Research DG535 delay generators to time, synchronize, and trigger charging power supplies and high-level thyratron trigger pulse generators. This timing system has been upgraded to use an SRS DG645 instrument due to reliability issues with the aforementioned model and slow response time of GPIB buses. The new model provides the relative timing of the separate kicker modules of the assembly from a synchronized external trigger with the RF system. Specifications of the timing scheme, an algorithm to load settings synchronized with RHIC real-time events, and performance analysis of the software will be presented in the paper.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA068
About •	paper received ※ 12 July 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEMPR010	Anomaly Detection for CERN Beam Transfer Installations Using Machine Learning	detector, feedback, experiment, controls	1066
	T. Dewitte, W. Meert, E. Van Wolputte Katholieke Universiteit Leuven, Leuven, Belgium P. Van Trappen CERN, Geneva, Switzerland
	Reliability, availability and maintainability determine whether or not a large-scale accelerator system can be operated in a sustainable, cost-effective manner. Beam transfer equipment (e.g. kicker magnets) has potentially significant impact on the global performance of a machine complex. Identifying root causes of malfunctions is currently tedious, and will become infeasible in future systems due to increasing complexity. Machine Learning could automate this process. For this purpose a collaboration between CERN and KU Leuven was established. We present an anomaly detection pipeline which includes preprocessing, detection, postprocessing and evaluation. Merging data of different, asynchronous sources is one of the main challenges. Currently, Gaussian Mixture Models and Isolation Forests are used as unsupervised detectors. To validate, we compare to manual e-logbook entries, which constitute a noisy ground truth. A grid search allows for hyper-parameter optimization across the entire pipeline. Lastly, we incorporate expert knowledge by means of semi-supervised clustering with COBRAS.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEMPR010
About •	paper received ※ 30 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

Other Keywords

Page

MOPHA068

Improving Reliability of the Fast Extraction Kicker Timing Control at the AGS

software, timing, extraction, controls

373

P.K. Kankiya, J.P. Jamilkowski
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
The fast extraction kicker system at AGS to RHIC transport line uses Stanford Research DG535 delay generators to time, synchronize, and trigger charging power supplies and high-level thyratron trigger pulse generators. This timing system has been upgraded to use an SRS DG645 instrument due to reliability issues with the aforementioned model and slow response time of GPIB buses. The new model provides the relative timing of the separate kicker modules of the assembly from a synchronized external trigger with the RF system. Specifications of the timing scheme, an algorithm to load settings synchronized with RHIC real-time events, and performance analysis of the software will be presented in the paper.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA068

About •

paper received ※ 12 July 2019 paper accepted ※ 10 October 2019 issue date ※ 30 August 2020

Export •

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

WEMPR010

Anomaly Detection for CERN Beam Transfer Installations Using Machine Learning

detector, feedback, experiment, controls

1066

T. Dewitte, W. Meert, E. Van Wolputte
Katholieke Universiteit Leuven, Leuven, Belgium
P. Van Trappen
CERN, Geneva, Switzerland

Reliability, availability and maintainability determine whether or not a large-scale accelerator system can be operated in a sustainable, cost-effective manner. Beam transfer equipment (e.g. kicker magnets) has potentially significant impact on the global performance of a machine complex. Identifying root causes of malfunctions is currently tedious, and will become infeasible in future systems due to increasing complexity. Machine Learning could automate this process. For this purpose a collaboration between CERN and KU Leuven was established. We present an anomaly detection pipeline which includes preprocessing, detection, postprocessing and evaluation. Merging data of different, asynchronous sources is one of the main challenges. Currently, Gaussian Mixture Models and Isolation Forests are used as unsupervised detectors. To validate, we compare to manual e-logbook entries, which constitute a noisy ground truth. A grid search allows for hyper-parameter optimization across the entire pipeline. Lastly, we incorporate expert knowledge by means of semi-supervised clustering with COBRAS.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-WEMPR010

About •

paper received ※ 30 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)