Mostafavi Atabak, Friedmann Andreas
Fraunhofer Institute for Structural Durability and System Reliability LBF, Darmstadt, Hessen, 64289, Germany.
Technische Universität Darmstadt, Department of Mechanical Engineering, Darmstadt, Hessen, 64287, Germany.
Sci Data. 2024 Oct 9;11(1):1108. doi: 10.1038/s41597-024-03934-5.
Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings' outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters.
弗劳恩霍夫风力涡轮机数据集包含一台750瓦风力涡轮机的监测数据，包括加速度计和转速计，用于捕获结构响应、轴承振动和转速。此外，还测量了温度、风速和风向，同时从选定来源获取了天气状况。已经实施了各种损伤场景，包括质量不平衡、气动不平衡以及轴承外圈、内圈和滚子元件的损伤。时间序列数据的可用性使得该数据集非常适合基于机器学习和信号处理的状态监测应用。异构传感器的可用性创建了一个特别适合信息融合、数据融合、多传感器方法和整体监测的数据集。实验是在受控实验室环境之外的实际条件下进行的，从而引入了诸如可变转子速度、噪声、过载和其他环境因素等挑战。因此，该数据集适用于涉及不确定性量化和信号预处理的任务。本文档将详细介绍测试设备、实验程序、模拟损伤案例和测量参数。
