Speaker
Description
RF-conditioning is an essential pre-processing step of all normal conducting cavities. This time-intensive work can pose great risks to equipment and cavity if conditioning-effects such as multipacting, discharges or degassing aren’t taken seriously.
To reduce the workload for human personnel, it was proposed to develop a deep-learning based algorithm to conduct conditionings on its own. This algorithm is trained on experimental data recorded from various conditionings performed by several experimenters. During initial training, the algorithm is tasked to predict the experimenters’ action based on the power, pressure and frequencies recorded over the last seconds. So far within this project developed networks have been able to perform this task with very small deviations.
To teach the network to not only reproduce human behaviors, but identify the optimal course during conditioning, pre-trained networks were combined with reinforcement learning to enable continued learning during experiments. The results of those experiments shall be discussed in this paper.
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