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Oct 16 – 21, 2022
Marriott Newport News at City Center
US/Eastern timezone

A Smart Alarm for the CEBAF Injector

Oct 18, 2022, 1:30 PM
1h 30m
Poster Poster Poster Session


TENNANT, Christopher (Jefferson Lab)


We present initial results from a proof-of-concept “smart alarm” for the CEBAF injector. Because of the injector's large number of parameters and possible fault scenarios, it is highly desirable to have an autonomous alarm system that can quickly identify and diagnose unusual machine states. Our approach leverages a trained neural network to not only identify an anomalous machine state, but also to identify the root-cause by pinpointing the specific element or region responsible. We developed an inverse model trained on data collected during normal operations. Using the inverse model, measurements from the machine are used to compute machine settings, which are then compared to EPICS setpoints. Instances when predictions differ from EPICS setpoints by a user-defined threshold are flagged as anomalies, and the user is alerted to the issue. We present the results of our data collection efforts, model training and performance, and initial performance metrics.

Primary author

TENNANT, Christopher (Jefferson Lab)


ABELL, Dan (RadiaSoft LLC) EDELEN, Jonathan (RadiaSoft LLC) EIENSTEIN-CURTIS, Joshua (RadiaSoft LLC) FREEMAN, Brian (JLAB) KAZIMI, Reza (Jefferson Lab) MOSER, Daniel (Jefferson Lab)

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