2024/01/28 by Zhiyuan Chen, Chen, Zhiyuan, Wei Lu +9 · 1 citation
Computer Science · Physics and Astronomy · #Accelerator Physics (physics.acc-ph) #Anomaly Detection Techniques and Applications #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Gamma-ray bursts and supernovae #I.5.4 #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2401.15543
openalex publication_date 2024/01/28 · openalex created_date 2024/01/31 · openalex updated_date 2026/07/28
A stable, reliable, and controllable orbit lock system is crucial to an electron (or ion) accelerator because the beam orbit and beam energy instability strongly affect the quality of the beam delivered to experimental halls. Currently, when the orbit lock system fails operators must manually intervene. This paper develops a Machine Learning based fault detection methodology to identify orbit lock anomalies and notify accelerator operations staff of the off-normal behavior. Our method is unsupervised, so it does not require labeled data. It uses Long-Short Memory Networks (LSTM) Auto Encoder to capture normal patterns and predict future values of monitoring sensors in the orbit lock system. Anomalies are detected when the prediction error exceeds a threshold. We conducted experiments using monitoring data from Jefferson Lab's Continuous Electron Beam Accelerator Facility (CEBAF). The results are promising: the percentage of real anomalies identified by our solution is 68.6%-89.3% using monitoring data of a single component in the orbit lock control system. The accuracy can be as high as 82%.