Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

arXiv:2404.15829v1 Announce Type: cross
Abstract: Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a LSTM-CNN binary classifier to distinguish between radio-frequency (RF) signals during normal operation and …

abstract accelerator arxiv continuous cs.lg deep learning delivery electron experimental facility integral laboratory part physics.acc-ph prediction study type