19–24 May 2024
Music City Center
US/Central timezone

SRF cavity fault prediction using deep learning at Jefferson Lab

SUPG034
19 May 2024, 14:00
4h
Bluegrass (MCC Exhibit Hall A)

Bluegrass

MCC Exhibit Hall A

201 Rep. John Lewis Way S, Nashville, TN 37203, USA
Student Poster Presentation MC6.D13 Machine Learning Student Poster Session

Speaker

Monibor Rahman (Old Dominion University)

Description

In this study, we present a deep learning-based pipeline for predicting superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. We leverage pre-fault RF signals from C100-type cavities and employ deep learning to predict faults in advance of their onset. We train a binary classifier model to distinguish between stable and impending fault signals, where each cryomodule has a uniquely trained model. Test results show accuracies exceeding 99% in each of the six models for distinguishing between normal signals and pre-fault signals from a class of more slowly developing fault types, such as microphonics-induced faults. We describe results from a proof-of-principle demonstration on a realistic, imbalanced data set and report performance metrics. Encouraging results suggest that future SRF systems could leverage this framework and implement measures to mitigate the onset in more slowly developing fault types.

Funding Agency

This work is supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177.

Region represented North America
Paper preparation format Word

Primary author

Monibor Rahman (Old Dominion University)

Co-authors

Khan Iftekharuddin (Old Dominion University) Adam Carpenter (Thomas Jefferson National Accelerator Facility) Chris Tennant (Thomas Jefferson National Accelerator Facility)

Presentation materials

There are no materials yet.