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RIS citation export for TUPDP138: Exploratory Data Analysis on the RHIC Cryogenics System Compressor Dataset

TY  - CONF
AU  - Gao, Y.
AU  - Brown, K.A.
AU  - Michnoff, R.J.
AU  - Nguyen, L.K.
AU  - Tran, A.D.
AU  - Zarcone, A.Z.
AU  - van Kuik, B.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Exploratory Data Analysis on the RHIC Cryogenics System Compressor Dataset
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - 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. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 907
EP  - 912
KW  - cryogenics
KW  - operation
KW  - network
KW  - data-analysis
KW  - controls
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TUPDP138
UR  - https://jacow.org/icalepcs2023/papers/tupdp138.pdf
ER  -