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Full-Text Articles in Engineering
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 …
Initial Implementation Of A Machine Learning System For Srf Cavity Fault Classification At Cebaf, A. Carpenter, K. M. Iftekharuddin, T. Powers, Y. Roblin, A. Solopava Shabalina, C. Tennant, L. Vidyaratne
Initial Implementation Of A Machine Learning System For Srf Cavity Fault Classification At Cebaf, A. Carpenter, K. M. Iftekharuddin, T. Powers, Y. Roblin, A. Solopava Shabalina, C. Tennant, L. Vidyaratne
Electrical & Computer Engineering Faculty Publications
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a high power Continuous Wave (CW) electron accelerator. It uses a mixture of of SRF cryomodules: older, lower energy C20/C50 modules and newer, higher energy C100 modules. The cryomodules are arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from the control system and saved for review. Analysis …