Abstract

The European X-ray Free Electron Laser (XFEL) is the worlds largest linear particle accelerator, reliant on precise timing measurements achieved through an optical synchronization system. The project aims to develop a predictive maintenance module for the optical synchronization system, using data processing techniques like dimensionality reduction and feature extraction to handle the high dimensionality and differing update rates of the data. Machine learning techniques, such as clustering, anomaly detection, classification, and forecasting, are utilized to determine the health state of the system. The overarching objective is to develop a predictive maintenance strategy that can anticipate and prevent system failures before they occur.