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%.

发展了一种基于机器学习的故障检测方法，利用 LSTM 自编码器来捕捉正常模式并预测轨道锁定系统中监测传感器的未来值，当预测误差超过阈值时检测到异常。使用连续电子束加速器设施的监测数据进行实验，结果表明，使用单个轨道锁定控制系统组件的监测数据，我们的解决方案可以识别出 68.6%-89.3% 的实际异常，准确率可以高达 82%。