Author: Freeman, B.G.
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.
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About • Received ※ 10 October 2021       Accepted ※ 19 January 2022       Issue date ※ 14 March 2022  
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