Unsupervised Anomaly Detection in ALS EPICS Event Logs

• URL: http://arxiv.org/abs/2509.13621v1
• Date: Wed, 17 Sep 2025 01:36:24 GMT
• Title: Unsupervised Anomaly Detection in ALS EPICS Event Logs
• Authors: Antonin Sulc, Thorsten Hellert, Steven Hunt,
• Abstract summary: 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.<n>By treating log entries as natural language, we transform them into contextual vector representations using semantic embedding techniques.<n>A sequence-aware neural network, trained on normal operational data, assigns a real-time anomaly score to each event.
• Score: 0.3058685580689604
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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