A smart alarm for particle accelerator beamline operations. Issue 1 (16th February 2023)

• Record Type: Journal Article
• Authors: Tennant, Chris; Freeman, Brian; Kazimi, Reza; Moser, Daniel; Abell, Dan; Edelen, Jonathan; Einstein-Curtis, Joshua
• Is Part Of: Machine learning: science and technology. Volume 4:Issue 1(2023)
• Publication Date: 2023-02-16
• Subjects: particle accelerator -- machine learning -- operations -- alarm system -- anomaly detection; 006.31
• DOI: 10.1088/2632-2153/acb98d
• Languages: English
• ISSNs: 2632-2153

Abstract

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