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Vidyaratne, L. S.

Publications and source records attributed to Vidyaratne, L. S..

5 records
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DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗
DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Carpenter, A.↗
DOE OSTI2022

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

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.

Rahman, M.↗
DOE OSTI2022

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.

Carpenter, A.↗
DOE OSTI2022

Field Emission Mitigation in CEBAF SRF Cavities Using Deep Learning

The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.

Ahammed, K.↗