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Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory
Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99. 99% accuracy and correctly predict 80% of slowly developing faults.
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information
Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information.
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab.
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