Author: Amatya, V.C.
Paper Title Page
WEPA40 The L-CAPE Project at FNAL 719
 
  • M. Jain, V.C. Amatya, G.U. Panapitiya, J.F. Strube
    PNNL, Richland, Washington, USA
  • B.F. Harrison, K.J. Hazelwood, W. Pellico, B.A. Schupbach, K. Seiya, J.M. St. John
    Fermilab, Batavia, Illinois, USA
  
  The con­trols sys­tem at FNAL records data asyn­chro­nously from sev­eral thou­sand Linac de­vices at their re­spec­tive ca­dences, rang­ing from 15Hz down to once per minute. In case of down­times, cur­rent op­er­a­tions are mostly re­ac­tive, in­ves­ti­gat­ing the cause of an out­age and la­bel­ing it after the fact. How­ever, as one of the most up­stream sys­tems at the FNAL ac­cel­er­a­tor com­plex, the Linac’s fore­knowl­edge of an im­pend­ing down­time as well as its du­ra­tion could prompt down­stream sys­tems to go into standby, po­ten­tially lead­ing to en­ergy sav­ings. The goals of the Linac Con­di­tion Anom­aly Pre­dic­tion of Emer­gence (L-CAPE) pro­ject that started in late 2020 are (1) to apply data-an­a­lytic meth­ods to im­prove the in­for­ma­tion that is avail­able to op­er­a­tors in the con­trol room, and (2) to use ma­chine learn­ing to au­to­mate the la­bel­ing of out­age types as they occur and dis­cover pat­terns in the data that could lead to the pre­dic­tion of out­ages. We pre­sent an overview of the chal­lenges in deal­ing with time-se­ries data from 2000+ de­vices, our ap­proach to de­vel­op­ing an ML-based au­to­mated out­age la­bel­ing sys­tem, and the sta­tus of aug­ment­ing op­er­a­tions by iden­ti­fy­ing the most likely de­vices pre­dict­ing an out­age.  
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DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEPA40  
About • Received ※ 03 August 2022 — Revised ※ 12 August 2022 — Accepted ※ 17 August 2022 — Issue date ※ 31 August 2022
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