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

@inproceedings{gao:icalepcs2023-tupdp138,
  author       = {Y. Gao and K.A. Brown and R.J. Michnoff and L.K. Nguyen and A.D. Tran and A.Z. Zarcone and B. van Kuik},
% author       = {Y. Gao and K.A. Brown and R.J. Michnoff and L.K. Nguyen and A.D. Tran and A.Z. Zarcone and others},
% author       = {Y. Gao and others},
  title        = {{Exploratory Data Analysis on the RHIC Cryogenics System Compressor Dataset}},
% booktitle    = {Proc. ICALEPCS'23},
  booktitle    = {Proc. 19th Int. Conf. Accel. Large Exp. Phys. Control Syst. (ICALEPCS'23)},
  eventdate    = {2023-10-09/2023-10-13},
  pages        = {907--912},
  paper        = {TUPDP138},
  language     = {english},
  keywords     = {cryogenics, operation, network, data-analysis, controls},
  venue        = {Cape Town, South Africa},
  series       = {International Conference on Accelerator and Large Experimental Physics Control Systems},
  number       = {19},
  publisher    = {JACoW Publishing, Geneva, Switzerland},
  month        = {02},
  year         = {2024},
  issn         = {2226-0358},
  isbn         = {978-3-95450-238-7},
  doi          = {10.18429/JACoW-ICALEPCS2023-TUPDP138},
  url          = {https://jacow.org/icalepcs2023/papers/tupdp138.pdf},
  abstract     = {{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. }},
}