Publications
Publication Information
| Title | Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory | ||
| Authors | Christopher Tennant, Adam Carpenter, Thomas Powers, Lasitha Vidyaratne, K. Iftekharuddin, Md. Monibor Rahman, Anna Shablina | ||
| JLAB number | JLAB-ACP-21-3375 | ||
| LANL number | (None) | ||
| Other number | DOE/OR/23177-5195 | ||
| Document Type(s) | (Meeting) | ||
| Category: | Diagnostics & Instrumentation |
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| Associated with EIC: | No | ||
| Supported by Jefferson Lab LDRD Funding: | Yes | ||
| LDRD Numbers: | |||
| Funding Source: | Nuclear Physics (NP) | ||
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Meeting Paper compiled for IPAC21 Proceedings Proceedings of IPAC 2021 Edited By Liu Lin, John M. Byrd, Regis Neuenschwander, Renan Picoreti, Volker R. W. Schaa JACOW (2021) Page(s) 4535-4539 | |||
| Publication Abstract: | We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of the eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time consuming. By leveraging machine learning, near real-time ? rather than postmortem ? identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques, and present preliminary work on cavity and fault prediction using data collected prior to a failure event. | ||
| Experiment Numbers: | other | ||
| Group: | Ctr for Adv Stud of Accel | ||
| Document: | |||
| DOI: | https://doi.org/10.18429/JACoW-IPAC2021-FRXC01 | ||
| Accepted Manuscript: | frxc01.pdf | ||
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