Operational data for fault prognosis in particle accelerators with machine learning.

ABSTRACT: This paper presents real operational data collected from the power systems of the Spallation Neutron Source facility, which provides the most intense neutron beam in the world. The authors have used a radio-frequency test facility (RFTF) and simulated system failures in the lab without causing a catastrophic system failure. Waveform signals have been collected from the RFTF normal operation as well as during fault induction efforts. The dataset provides a significant amount of normal and faulty signals for the training of statistical or machine learning models. Then, the authors performed 21 test experiments, where the faults are slowly induced into the RFTF system for the purpose of testing the models in fault prognosis to detect and prevent impending faults. The test experiments include

SUBMITTER: Radaideh MI 

PROVIDER: S-EPMC10622613 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature