Author: Abell, D.T.
Paper Title Page
WEPV023 Development of a Smart Alarm System for the CEBAF Injector 691
 
  • D.T. Abell, J.P. Edelen
    RadiaSoft LLC, Boulder, Colorado, USA
  • B.G. Freeman, R. Kazimi, D.G. Moser, C. Tennant
    JLab, Newport News, Virginia, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682.
RadiaSoft and Jefferson Laboratory are working together to develop a machine-learning-based smart alarm system for the CEBAF injector. Because of the injector’s large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. We present our work on artificial neural networks designed to identify such undesirable machine states. In particular, we test both auto-encoders and inverse models as possible tools for differentiating between normal and abnormal states. These models are being developed using both supervised and unsupervised learning techniques, and are being trained using CEBAF injector data collected during dedicated machine studies as well as during regular operations. Lastly, we discuss tradeoffs between the two types of models.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV023  
About • Received ※ 10 October 2021       Accepted ※ 19 January 2022       Issue date ※ 14 March 2022  
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WEPV024 X-Ray Beamline Control with Machine Learning and an Online Model 695
 
  • B. Nash, D.T. Abell, D.L. Bruhwiler, E.G. Carlin, J.P. Edelen, M.V. Keilman, P. Moeller, R. Nagler, I.V. Pogorelov, S.D. Webb
    RadiaSoft LLC, Boulder, Colorado, USA
  • Y. Du, A. Giles, J. Lynch, J. Maldonado, M.S. Rakitin, A. Walter
    BNL, Upton, New York, USA
 
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract DE-SC0020593.
We present recent developments on control of x-ray beamlines for synchrotron light sources. Effective models of the x-ray transport are updated based on diagnostics data, and take the form of simplified physics models as well as learned models from scanning over mirror and slit configurations. We are developing this approach to beamline control in collaboration with several beamlines at the NSLS-II. By connecting our online models to the Blue-Sky framework, we enable a convenient interface between the operating machine and the model that may be applied to beamlines at multiple facilities involved in this collaborative software development.
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV024  
About • Received ※ 10 October 2021       Accepted ※ 21 November 2021       Issue date ※ 17 December 2021  
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