The European X-Ray Free-Electron Laser (EuXFEL) relies like other high integrity systems on several sub systems. The Low Level Radio Frequency (LLRF) sub system of the EuXFEL is responsible for the correct acceleration of electron bunches. The LLRF system comprises several embedded components that are directly connected to the accelerator hardware. Due to the high complexity of the LLRF system, unforeseen machine trips occur regularly.In this work we built the basis for a mechanism that automatically identifies faulty behavior of the embedded components. To achieve that, we performed two different experiments, where a faulty behavior was artificially injected to the system. We analyzed the experiment data, performed a feature extraction and applied different machine learning methods. We used basic anomaly detection and basic clustering methods for identifying the faulty data elements. Additionally, we used a support vector machine for modelling the systems behavior. The selected algorithms are compared with respect to their ability to classify LLRF data correctly.
