Wind turbine condition monitoring dataset of Fraunhofer LBF
收藏资源简介：
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.
弗劳恩霍夫风机数据集（Fraunhofer wind turbine dataset）收录了一台750瓦风力发电机组（WT，wind turbine）的监测数据，通过加速度计与转速计采集结构响应、轴承振动及转子转速参数。此外，该数据集还同步采集了结构温度、风速与风向数据，气象相关信息取自选定的数据源。数据集设置了多类典型损伤工况，涵盖质量不平衡、气动不平衡，以及轴承外圈、内圈与滚动体损伤。得益于完备的时序数据，该数据集可很好地适配机器学习与基于信号处理的状态监测（CM，condition monitoring）两类应用场景。由于采用多类型异构传感器进行数据采集，该数据集尤其适用于信息融合、数据融合、多传感器方法以及全域状态监测相关研究。本次实验均在非受控实验室的真实户外工况下开展，因此引入了转子转速波动、环境噪声、载荷过载及其他随机环境因素等真实挑战。综上，该数据集可胜任涉及不确定性量化与信号预处理的各类研究任务。本文档将详细阐述测试设备、实验流程、模拟损伤工况、测量参数、数据细节，以及用于验证数据质量的初步分析内容。
