Keyword: data-analysis
Paper Title Other Keywords Page
TUPDP138 Exploratory Data Analysis on the RHIC Cryogenics System Compressor Dataset cryogenics, operation, network, controls 907
 
  • Y. Gao, K.A. Brown, R.J. Michnoff, L.K. Nguyen, A.Z. Zarcone, B. van Kuik
    BNL, Upton, New York, USA
  • A.D. Tran
    FRIB, East Lansing, Michigan, USA
 
  Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
The Relativistic Heavy Ion Collider (RHIC) Cryogenic Refrigerator System is the cryogenic heart that allows RHIC superconducting magnets to operate. Parts of the refrigerator are two stages of compression composed of ten first and five second-stage compressors. Compressors are critical for operations. When a compressor faults, it can impact RHIC beam operations if a spare compressor is not brought online as soon as possible. The potential of applying machine learning to detect compressor problems before a fault occurs would greatly enhance Cryo operations, allowing an operator to switch to a spare compressor before a running compressor fails, minimizing impacts on RHIC operations. In this work, various data analysis results on historical compressor data are presented. It demonstrates an autoencoder-based method, which can catch early signs of compressor trips so that advance notices can be sent for the operators to take action.
 
poster icon Poster TUPDP138 [2.897 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP138  
About • Received ※ 05 October 2023 — Revised ※ 22 October 2023 — Accepted ※ 30 November 2023 — Issued ※ 11 December 2023
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