A smart alarm for particle accelerator beamline operations

Abstract We present the initial results of a proof-of-concept ‘smart alarm’ for Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify diagnose unusual states is needed. Our approach leverages trained neural network capable alerting operators (a) when anomalous condition exists in (b) identifying element setting that root cause. The tool based on inverse model maps readings (diagnostic readbacks) settings (beamline attributes can modify). takes as input from computes which are compared control setpoints. Instances where predictions differ setpoints by user-defined threshold flagged anomalous. Given data corresponding 354 configurations, narrow cause three potential candidates with 94.6% accuracy. Furthermore, current method conditions raises parameters drift outside their normal tolerances, data-driven 83% more conditions.