Author: Charifoulline, Z.
Paper Title Page
MOPAB345 Machine Learning with a Hybrid Model for Monitoring of the Protection Systems of the LHC 1072
 
  • C. Obermair, A. Apollonio, Z. Charifoulline, M. Maciejewski, A.P. Verweij
    CERN, Geneva, Switzerland
  • C. Obermair, F. Pernkopf
    TUG, Graz, Austria
 
  The LHC is the world’s largest par­ti­cle ac­cel­er­a­tor and uses a com­plex set of so­phis­ti­cated and highly re­li­able ma­chine pro­tec­tion sys­tems to en­sure a safe op­er­a­tion with high avail­abil­ity for par­ti­cle physics pro­duc­tion. The data gath­ered dur­ing sev­eral years of suc­cess­ful op­er­a­tion allow the use of data-dri­ven meth­ods to as­sist ex­perts in find­ing anom­alies in the be­hav­ior of those pro­tec­tion sys­tems. In this paper, we de­rive a model that can ex­tend the ex­ist­ing sig­nal mon­i­tor­ing ap­pli­ca­tions for the LHC pro­tec­tion sys­tems with ma­chine learn­ing. Our hy­brid model com­bines an ex­ist­ing thresh­old-based sys­tem with a SVM by using sig­nals, man­u­ally val­i­dated by ex­perts. Even with a lim­ited amount of data, the SVM learns to in­te­grate the ex­pert knowl­edge and con­tributes to a bet­ter clas­si­fi­ca­tion of safety-crit­i­cal sig­nals. Using this ap­proach, we an­a­lyze his­tor­i­cal sig­nals of quench heaters, which are an im­por­tant part of the quench pro­tec­tion sys­tem for su­per­con­duct­ing mag­nets. Par­tic­u­larly, it is pos­si­ble to in­cor­po­rate ex­pert de­ci­sions into the clas­si­fi­ca­tion process and to im­prove the fail­ure de­tec­tion rate of the ex­ist­ing quench heater dis­charge analy­sis tool.  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB345  
About • paper received ※ 20 May 2021       paper accepted ※ 19 July 2021       issue date ※ 01 September 2021  
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