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Publication Information
Title Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory
Abstract Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a LSTM-CNN binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these a
Author(s) Md Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Christopher Tennant
Publication Date September 2024
Category Computational Physics
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Accelerator Ops, R&D / Cntr-Adv Studies of Acce / Ctr for Adv Stud of Accel
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-ACP-24-4013 OSTI Number: 2447362
LANL Number: Other Number: DOE/OR/23177-7442
Associated with an experiment No
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Machine Learning: Science and Technology
Refereed Yes
Volume 5
Issue 035078
Page(s)
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