Automated Anomaly Detection on European XFEL Klystrons

TL;DR

This paper explores machine learning techniques to analyze European XFEL klystron data, aiming to identify operational states and early faults to reduce maintenance time and improve reliability.

Contribution

It applies advanced data-driven methods to klystron operation data, enabling early anomaly detection and better understanding of operational modes.

Findings

Identified key features for normal operation

Recognized promising components for fault detection

Demonstrated effectiveness of machine learning in anomaly detection

Abstract

High-power multi-beam klystrons represent a key component to amplify RF to generate the accelerating field of the superconducting radio frequency (SRF) cavities at European XFEL. Exchanging these high-power components takes time and effort, thus it is necessary to minimize maintenance and downtime and at the same time maximize the device's operation. In an attempt to explore the behavior of klystrons using machine learning, we completed a series of experiments on our klystrons to determine various operational modes and conduct feature extraction and dimensionality reduction to extract the most valuable information about a normal operation. To analyze recorded data we used state-of-the-art data-driven learning techniques and recognized the most promising components that might help us better understand klystron operational states and identify early on possible faults or anomalies.