Author: Solopova Shabalina, A.D.
Paper Title Page
WEPHA025 Initial Implementation of a Machine Learning System for SRF Cavity Fault Classification at CEBAF 1131
  • A. Carpenter, T. Powers, Y. Roblin, A.D. Solopova Shabalina, C. Tennant
    JLab, Newport News, Virginia, USA
  • K.M. Iftekharuddin, L. Vidyaratne
    ODU, Norfolk, Virginia, USA
  Funding: Authored by Jefferson Science Associates, LLC under U.S. DOE Contract No. DE-AC05-06OR23177
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a high power Continuous Wave (CW) electron accelerator. It uses a mixture of of SRF cryomodules: older, lower energy C20/C50 modules and newer, higher energy C100 modules. The cryomodules are arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from the control system and saved for review. Analysis of these waveforms is time intensive and requires a subject matter expert (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.
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About • paper received ※ 30 September 2019       paper accepted ※ 09 October 2019       issue date ※ 30 August 2020  
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