Speakers
Description
Ensuring 24/7 operation of the laser systems for DESY's free-electron lasers requires detailed analysis of operator logbooks and RT-tickets, a task currently limited by labor-intensive manual methods that lack statistical granularity. As part of a DASHH collaboration with the University of Hamburg, we investigate the application of Large Language Models (LLMs) to automate this analysis. We benchmarked LLMs on binary issue detection in logbooks and multi-class categorization of intervention tickets, achieving strong performance with an F1 score of approximately 0.84 and a Macro-F1 of 0.42, respectively. This automated classification enables fine-grained, real-time statistical analysis of fault patterns for data-driven identification of subsystems requiring targeted upgrades. This work provides the foundation for developing advanced operational tools, including system health dashboards, expert support chatbots, and proactive diagnostic agents.
