Speaker
Thorsten Hellert
(Lawrence Berkeley National Laboratory)
Description
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.
Author
Antonin Sulc
(Lawrence Berkeley National Laboratory)
Co-authors
Steven Hunt
(Lawrence Berkeley National Laboratory)
Thorsten Hellert
(Lawrence Berkeley National Laboratory)
