{"ID":2869625,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13621","arxiv_id":"2509.13621","title":"Unsupervised Anomaly Detection in ALS EPICS Event Logs","abstract":"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.","short_abstract":"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 n...","url_abs":"https://arxiv.org/abs/2509.13621","url_pdf":"https://arxiv.org/pdf/2509.13621v1","authors":"[\"Antonin Sulc\",\"Thorsten Hellert\",\"Steven Hunt\"]","published":"2025-09-17T01:36:24Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}