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.

本数据集仓库展示了从散裂中子源（Spallation Neutron Source）设施的电力系统采集的真实运行数据，该设施以输出全球最强中子束流而闻名。本数据集可为开发旨在预先识别系统故障、实现运维人员及时干预及高效运维监管的技术与算法提供宝贵资源。研究团队借助无线电频率测试设施（radio-frequency test facility, RFTF）开展了模拟系统故障的可控实验室实验，所有实验均未触发灾难性系统瘫痪。本数据集包含系统正常运行及人为引入故障过程中采集的波形信号，可为统计模型或机器学习模型的训练提供海量数据支撑。随后，研究团队开展了21次测试实验，在RFTF系统中逐步引入故障，以评估模型检测并预警潜在故障的有效性。此类实验结合了磁通量补偿与启动脉冲宽度调整操作，使得系统输出电压、电流等多项波形指标逐步劣化，有效模拟了真实故障场景。所有实验于2022年7月在美国田纳西州橡树岭市橡树岭国家实验室的散裂中子源设施内完成。本数据集的适用人群涵盖控制、预测性维护、机器学习及信号处理领域的研究人员。