Keyword: kicker
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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 ※  
About • paper received ※ 12 July 2019       paper accepted ※ 10 October 2019       issue date ※ 30 August 2020  
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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 ※  
About • paper received ※ 30 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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