This paper introduces an automated fault analysis framework for the Advanced Light Source (ALS) that processes real-time event logs from its EPICS control system. By treating log entries as natural language, we transform them into contextual vector representations using semantic embedding techniques. A sequence-aware neural network, trained on normal operational data, assigns a real-time anomaly score to each event. This method flags deviations from baseline behavior, enabling operators to rapidly identify the critical event sequences that precede complex system failures.
@article{arxiv.2509.13621,
title = {Unsupervised Anomaly Detection in ALS EPICS Event Logs},
author = {Antonin Sulc and Thorsten Hellert and Steven Hunt},
journal= {arXiv preprint arXiv:2509.13621},
year = {2025}
}
Comments
6 pages, 5 figures, The 20th International Conference on Accelerator and Large Experimental Physics Control Systems