| Title |
Initial Studies of Cavity Fault Prediction at Jefferson Laboratory |
| Authors |
- L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D.L. Turner
JLab, Newport News, Virginia, USA
- K.M. Iftekharuddin, M. Rahman
ODU, Norfolk, Virginia, USA
|
| 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
|
| Funding |
This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177. |
| Paper |
download WEPV025.PDF [1.057 MB / 5 pages] |
| Poster |
download WEPV025_POSTER.PDF [1.111 MB] |
| Cite |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| Conference |
ICALEPCS2021 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (18th) |
| Location |
Shanghai, China |
| Date |
14-22 October 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Kazuro Furukawa (KEK, Tsukuba, Japan); Yingbing Yan (SARI,Shanghai, China); Yongbin Leng (SARI,Shanghai, China); Zhichu Chen (SARI,Shanghai, China); Volker RW Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-221-9 |
| Online ISSN |
2226-0358 |
| Received |
08 October 2021 |
| Revised |
19 October 2021 |
| Accepted |
19 December 2021 |
| Issue Date |
11 February 2022 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2021-WEPV025 |
| Pages |
700-704 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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