JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{vidyaratne:icalepcs2021-wepv025,
author = {L.S. Vidyaratne and A. Carpenter and K.M. Iftekharuddin and M. Rahman and R. Suleiman and C. Tennant and D.L. Turner},
% author = {L.S. Vidyaratne and A. Carpenter and K.M. Iftekharuddin and M. Rahman and R. Suleiman and C. Tennant and others},
% author = {L.S. Vidyaratne and others},
title = {{Initial Studies of Cavity Fault Prediction at Jefferson Laboratory}},
booktitle = {Proc. ICALEPCS'21},
pages = {700--704},
eid = {WEPV025},
language = {english},
keywords = {cavity, cryomodule, SRF, electron, data-acquisition},
venue = {Shanghai, China},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {18},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {03},
year = {2022},
issn = {2226-0358},
isbn = {978-3-95450-221-9},
doi = {10.18429/JACoW-ICALEPCS2021-WEPV025},
url = {https://jacow.org/icalepcs2021/papers/wepv025.pdf},
abstract = {{The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach and report on initial results. Recent modifications to the low-level RF control system will provide access to streaming signals and we outline a path forward for leveraging deep learning on streaming data}},
}