SRF Cavity Fault Classification Using Machine Learning At CEBAF

Abstract

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab is the first large high power CW recirculating electron accelerator to make use of SRF accelerating structures. The structures are configured in two anti-parallel linacs connected by arcs. Each linac consists of twenty C20/C50 cryomodules each containing eight 5-cell cavities and five C100 upgrade cryomodules each containing eight 7-cell cavities. Accurately classifying the source of cavity faults is critical for improving accelerator performance. A cavity fault triggers a waveform acquisition process where 17 waveform records sampled at 5 kHz are recorded for each of the 8 cavities in the affected cryomodule. The waveform record length is sufficiently long for transient microphonic effects to be observable. This data combined with archived signals sampled at 10 Hz are used to classify faults. Significant time is required for a subject matter expert to analyze and identify the intra-cavity signatures of imminent faults. This paper describes a path forward that utilizes machine learning for automatic fault classification. Post-training identification of the physical origins of faults are discussed, as are potential machine-trained model-free implementations of trip avoidance procedures. These methods should provide new insights into cavity fault mechanisms and facilitate intelligent optimization of cryomodule performance