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.

为探索通过机器学习研究速调管的行为，我们完成了一系列实验，确定了各种操作模式，并进行了特征提取和降维，以提取有关正常运行的最有价值信息，使用先进的数据驱动学习技术分析记录的数据，识别可能有助于更好地理解速调管运行状态并及早发现故障或异常的最有前途的组件。