Vidyaratne, L.S. Carpenter, A. Iftekharuddin, K.M. Rahman, M. Suleiman, R. Tennant, C. Turner, D.L. JACoW Publishing Geneva, Switzerland 2226-0358 978-3-95450-221-9 10.18429/JACoW-ICALEPCS2021-WEPV025 English 700-704 cavity cryomodule SRF electron data-acquisition Contribution to a conference proceedings 2022 2022-03 https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV025 https://jacow.org/icalepcs2021/papers/wepv025.pdf 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