Anomaly Detection of Particle Orbit in Accelerator using LSTM Deep Learning Technology

TL;DR

This paper presents an unsupervised LSTM autoencoder approach for detecting anomalies in accelerator orbit lock systems, improving fault detection without needing labeled data and achieving up to 89.3% detection rate.

Contribution

It introduces a novel unsupervised anomaly detection method using LSTM autoencoders for accelerator orbit systems, eliminating the need for labeled training data.

Findings

Detection rate of up to 89.3% for real anomalies.

Prediction accuracy reaching 82%.

Effective in identifying off-normal behavior in accelerator systems.

Abstract

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%.