  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.
