Wind turbine condition monitoring dataset of Fraunhofer LBF

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine (WT), including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures of the structure, 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 (CM) 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, measurement parameters, data specifics, and preliminary analysis aimed at validating data quality. See the full data descriptor at: https://doi.org/10.1038/s41597-024-03934-5弗劳恩霍夫风机数据集（Fraunhofer wind turbine dataset）包含一台750瓦级风力发电机组（WT）的监测数据，搭载加速度计与转速计，用于采集结构响应、轴承振动及旋转速度信息。此外，该数据集还涵盖机组结构温度、风速与风向的实测数据，并通过选定数据源获取了气象环境信息。研究人员已针对多种损伤工况进行模拟植入，包括质量不平衡、气动不平衡，以及轴承外圈、内圈与滚动体损伤。该数据集包含时序数据，非常适用于基于机器学习与信号处理的状态监测（Condition Monitoring，简称CM）任务。多类异构传感器的配置使得该数据集尤其适用于信息融合、数据融合、多传感器方法及全维度状态监测研究。实验均在非受控实验室环境的真实工况下开展，因此引入了转子转速波动、噪声、过载及其他环境干扰等实际挑战。正因如此，该数据集可有效应用于涉及不确定性量化与信号预处理的相关任务。本文档将详细阐述试验设备、实验流程、模拟损伤工况、测量参数、数据细节及用于验证数据质量的初步分析内容。完整数据描述请参见：https://doi.org/10.1038/s41597-024-03934-5