Operational Data for Fault Prognosis in Particle Accelerators with Machine Learning

This repository showcases real-world operational data gathered from the power systems of the Spallation Neutron Source facility, renowned for delivering the world's most intense neutron beam. This dataset serves as a valuable resource for crafting techniques and algorithms aimed at preemptively identifying system faults, enabling timely operator intervention, and effective maintenance oversight. The authors utilized a radio-frequency test facility (RFTF) to conduct controlled laboratory experiments simulating system failures, all without triggering a catastrophic system breakdown. The dataset comprises waveform signals obtained during both regular system operations and deliberate fault induction efforts, offering a substantial amount of data for training statistical or machine learning models. Afterward, the authors carried out 21 test experiments wherein they gradually introduced faults into the RFTF system to evaluate the models' effectiveness in detecting and preempting impending faults. These experiments involved combinations of magnetic flux compensation and adjustments to start pulse width, leading to a gradual deterioration in various waveform aspects such as system output voltage and current. These alterations effectively mimicked real fault scenarios. All experiments took place at the Oak Ridge National Laboratory's Spallation Neutron Source facility in Oak Ridge, Tennessee, United States, during July 2022. The users of this dataset may include researchers in control, predictive maintenance, machine learning, and signal processing.