Document Type
Conference Paper
Publication Date
2019
DOI
10.18429/JACoW-ICALEPCS2019-WEPHA025
Publication Title
Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems
Pages
1131-1135
Conference Name
17th International Conference on Accelerator and Large Experimental Physics Control Systems, 7-11 October 2019, New York City, New York
Abstract
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a high power Continuous Wave (CW) electron accelerator. It uses a mixture of of SRF cryomodules: older, lower energy C20/C50 modules and newer, higher energy C100 modules. The cryomodules are arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from the control system and saved for review. Analysis of these waveforms is time intensive and requires a subject matter expert (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.
Rights
© 2019 JACoW.
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.
Original Publication Citation
Carpenter, A., Iftekharuddin, K. M., Powers, T., Roblin, Y., Solopova Shabalina, A. D., Tennant, C., & Vidyaratne, L. (2019). Initial implementation of a machine learning system for SRF cavity fault classification at CEBAF. In K. White, K. Brown, P. Dyer, & V. R. W. Schaa (Eds.), Proceedings of the 17th International Conference on Accelerator and Large Experimental Physics Control Systems. JACoW Publishing. https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA025
Repository Citation
Carpenter, A.; Iftekharuddin, K. M.; Powers, T.; Roblin, Y.; Solopava Shabalina, A.; Tennant, C.; and Vidyaratne, L., "Initial Implementation of a Machine Learning System for SRF Cavity Fault Classification at CEBAF" (2019). Electrical & Computer Engineering Faculty Publications. 450.
https://digitalcommons.odu.edu/ece_fac_pubs/450
ORCID
0000-0001-8316-4163 (Iftekharuddin)