no code implementations • 24 Apr 2024 • Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Chris Tennant
Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99. 99% accuracy and correctly predict 80% of slowly developing faults.
no code implementations • 11 Jun 2020 • Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin
We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab.