Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep
  Learning Technology
Zhiyuan Chen Wei Lu Radhika Bhong Yimin Hu Brian Freeman Adam Carpenter
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%.