Building Datasets and Training Methods for ML Based Magnet Quench Detection

Conference · Tue Jul 08 04:00:00 EDT 2025 · No journal information

DOI:https://doi.org/10.2172/2571874· OSTI ID:2571874

Khan, Maira ^[1]; DiMarco, Joseph ^[1]; Krave, Steven T. ^[1]; Marinozzi, Vittorio ^[1]; Stoynev, Stoyan Emilov ^[1]; Ngadiuba, Jennifer ^[1]; Tran, Nhan ^[1]

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1. Fermilab

Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type, thermal cycle, and ramp. These events together build a more complete picture of continuous processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We present our work on building an automatic machine learning (ML) based quench detection system. We build upon our existing work on unsupervised auto-encoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework on our QA and acoustic data which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised neural network auto-encoder performance.This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate our quench prediction autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: US Department of Energy
• DOE Contract Number: 89243024CSC000002
• OSTI ID: 2571874
• Report Number(s): FERMILAB-POSTER-25-0065-CSAID-TD; oai:inspirehep.net:2945835
• Conference Information: Journal Name: No journal information
• Country of Publication: United States
• Language: English