Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Physics
Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)
Real-Time Cavity Fault Prediction In Cebaf Using Deep Learning, Md. M. Rahman, K. Iftekharuddin, A. Carptenter, T. Mcguckin, C. Tennant, L. Vidyaratne, Sandra Biedron (Ed.), Evgenya Simakov (Ed.), Stephen Milton (Ed.), Petr M. Anisimov (Ed.), Volker R.W. Schaa (Ed.)
Electrical & Computer Engineering Faculty Publications
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 …
Initial Studies Of Cavity Fault Prediction At Jefferson Laboratory, L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D. Turner, Khan Iftekharuddin, Md. Monibor Rahman
Initial Studies Of Cavity Fault Prediction At Jefferson Laboratory, L.S. Vidyaratne, A. Carpenter, R. Suleiman, C. Tennant, D. Turner, Khan Iftekharuddin, Md. Monibor Rahman
Electrical & Computer Engineering Faculty Publications
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linac that utilizes over 400 superconducting radio-frequency (SRF) cavities to accelerate electrons up to 12 GeV through 5-passes. Recent work has shown that, given RF signals from a cavity during a fault as input, machine learning approaches can accurately classify the fault type. In this paper we report on initial results of predicting a fault onset using only data prior to the failure event. A data set was constructed using time-series data immediately before a fault (’unstable’) and 1.5 seconds prior to a fault (’stable’) gathered …
Using Ai For Management Of Field Emission In Srf Linacs, A. Carpenter, P. Degtiarenko, R. Suleiman, C. Tennant, D. Turner, L. S. Vidyaratne, Khan Iftekharuddin, Md. Monibor Rahman
Using Ai For Management Of Field Emission In Srf Linacs, A. Carpenter, P. Degtiarenko, R. Suleiman, C. Tennant, D. Turner, L. S. Vidyaratne, Khan Iftekharuddin, Md. Monibor Rahman
Electrical & Computer Engineering Faculty Publications
Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high …
Cryogenic Probe Station At Old Dominion University Center For Accelerator Science, Junki Makita, Jean R. Delayen, Alex Gurevich, Gianluigi Ciovati
Cryogenic Probe Station At Old Dominion University Center For Accelerator Science, Junki Makita, Jean R. Delayen, Alex Gurevich, Gianluigi Ciovati
Physics Faculty Publications
With a growing effort in research and development of an alternative material to bulk Nb for a superconducting radiofrequency (SRF) cavity, it is important to have a cost effective method to benchmark new materials of choice. At Old Dominion University's Center for Accelerator Science, a cryogenic probe station (CPS) will be used to measure the response of superconductor samples under RF fields. The setup consists of a closed-cycle refrigerator for cooling a sample wafer to a cryogenic temperature, a superconducting magnet providing a field parallel to the sample, and DC probes in addition to RF probes. The RF probes will …