About: Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
Creator	Carpenter, Adam Iftekharuddin, Khan Powers, Tom Solopova, Anna Tennant, Chris Vidyaratne, Lasitha
Source	ArXiv
abstract	We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to 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 post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.
has issue date	2020-06-11(xsd:dateTime)
has license	arxiv
sha1sum (hex)	cc9cc2b8407d993993ea0742418565efe8b2c3c6
resource representing a document's title	Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
resource representing a document's body	covid:cc9cc2b8407d993993ea0742418565efe8b2c3c6#body_text
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