A Weakly Supervised Machine Learning Procedure for Magnet Quench Diagnostics

Conference · Tue Dec 23 00:00:00 EST 2025 · IEEE Trans.Appl.Supercond.

DOI:https://doi.org/10.1109/TASC.2025.3647009· OSTI ID:3011765

Khan, Maira ^[1]; Krave, Steven ^[1]; Marinozzi, Vittorio ^[1]; Ngadiuba, Jennifer ^[1]; Stoynev, Stoyan ^[1]; Tran, Nhan ^[1]

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1. Fermilab

Voltage taps remain the standard and reliable diagnostic tool for detecting quenches in superconducting magnets. However, they identify a quench only at the time of voltage rise and do not provide information on earlier physical precursors. In this work, we investigate whether acoustic emission data can reveal precursor activity that occurs before conventional voltage detection using machine learning techniques. We introduce an event selection method and a weakly supervised machine learning procedure to learn data-driven criteria for identifying potential acoustic precursors to quenches. Two Convolutional Neural Network (CNN) architectures are trained: one on acoustic sensor events from our selection procedure and one on the Fast Fourier Transforms (FFTs) of these events. Both networks are trained iteratively using confidence-weighted loss functions to associate certain subsets of training data with a precursor label. We evaluate the performance of these models by examining the time distribution of events classified as potential precursors relative to the quench onset. Results indicate that the proposed approach can possibly distinguish acoustic emission events occurring closer to the quench from earlier acoustic activity during ramping, suggesting the potential for flagging quench precursors in acoustic data.

• Research Organization: Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
• Sponsoring Organization: US Department of Energy
• DOE Contract Number: 89243024CSC000002
• OSTI ID: 3011765
• Report Number(s): FERMILAB-CONF-25-0535-CSAID-TD; oai:inspirehep.net:3097170
• Resource Type: Conference paper
• Conference Information: Journal Name: IEEE Trans.Appl.Supercond.
• Country of Publication: United States
• Language: English