Gao, Y. Brown, K.A. Michnoff, R.J. Nguyen, L.K. Tran, A.D. Zarcone, A.Z. van Kuik, B. JACoW Publishing Geneva, Switzerland 2226-0358 978-3-95450-238-7 10.18429/JACoW-ICALEPCS2023-TUPDP138 English 907-912 cryogenics operation network data-analysis controls Contribution to a conference proceedings 2024 2024-02 https://doi.org/10.18429/JACoW-ICALEPCS2023-TUPDP138 https://jacow.org/icalepcs2023/papers/tupdp138.pdf 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.