Publications
Publication Information
| Title | INITIAL STUDIES OF CAVITY FAULT PREDICTION AT JEFFERSON LABORATORY |
| Authors | Lasitha Vidyaratne, Adam Carpenter, Christopher Tennant, Riad Suleiman, Dennison Turner, K. Iftekharuddin, Md. Monibor Rahman |
| JLAB number | JLAB-ACC-21-3513 |
| LANL number | (None) |
| Other number | DOE/OR/23177-5390 |
| Document Type(s) | (Other) |
| Category: | Computational Physics |
| Associated with EIC: | No |
| Supported by Jefferson Lab LDRD Funding: | No |
| Funding Source: | Nuclear Physics (NP) |
|
Other Poster | |
| Publication Abstract: | The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is a CW recirculating linear accelerator (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 initial results of predicting a fault onset using only data prior to the failure event. A dataset was constructed using time-series data immediately before a fault (?unstable?) and 1.5 seconds prior to a fault (?stable?) gathered from over 5,000 saved fault events. The data was used to train a binary classifier. The results gave key insights into the behavior of several fault types and provided motivation to investigate whether data prior to a failure event could also predict the type of fault. We discuss our method using a sliding window approach. Based on encouraging initial results, we outline a path forward to leverage deep learning on streaming data for fault type prediction. |
| Experiment Numbers: | |
| Group: | Operations Support |
| Document: | pptx |
| DOI: | |
| Accepted Manuscript: | |
| Supporting Documents: | |
| Supporting Datasets: | |