  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.
