Document Type

Conference Paper

Publication Date




Publication Title

NAPAC 2022: Proceedings of the North American Particle Accelerator Conference




Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.


Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI.

Original Publication Citation

Rahman, M. M., Iftekharuddin, K., Carpenter, A., McGuckin, T., Tennant, C., & Vidyaratne, L. (2022). Real-time cavity fault prediction in CEBAF using deep learning. In S. Biedron, E. Simakov, S. Milton, P.M. Anisimov, V.R.W. Schaa (Eds), NAPAC 2022: Proceedings of the North American Particle Accelerator Conference (pp. 687-690). JACoW Publishing.


0000-0001-8316-4163 (Iftekharuddin)