Proactive, Not Reactive: Anticipating and Mitigating Faults in XFEL Photocathode Lasers

X-ray Free Electron Lasers (XFELs) are complex, large-scale facilities. They heavily rely on ultrafast lasers used to generate electrons to be accelerated (photocathode lasers). The XFEL’s X-ray output is highly sensitive to the performance of such lasers. Therefore, a laser failure will stop the entire facility. This project aims to develop an advanced distributed anomaly detection framework leveraging machine learning and particularly exploiting the unique opportunity of having five identical photocathode laser systems, NEPAL (Next Generation Photocathode Laser), at three DESY facilities. To facilitate fault localization and preemptive interventions, we will create a framework that identifies nominal and problematic behavior of (sub-)components. These sub-component models are combined, based on the physical system structure, into a visual representation, i.e., Digital Shadow, of the laser system itself. Our approach uniquely combines learning causal structures and influences between subsystems with machine learning-based anomaly detection. At the end of the project, we will have an initial model for the NEPAL laser systems equipped with the most critical subsystems. This model is continuously updated at runtime and can be used by facility operators to analyze the system health and to plan system adjustments, such as preventive maintenance interactions. The model setup will consider the possibility of future expansions, enabling semi-automatic system adjustments and fault localization