Unsupervised Anomaly Detection in ALS EPICS Event Logs
This addresses fault analysis for operators of the Advanced Light Source, but it is incremental as it applies existing semantic embedding and neural network techniques to a new domain-specific dataset.
The paper tackled the problem of automated fault detection in the Advanced Light Source control system by processing event logs as natural language and using semantic embeddings with a sequence-aware neural network to assign real-time anomaly scores, enabling rapid identification of critical event sequences preceding failures.
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.