https://fordatis.fraunhofer.de/handle/fordatis/81 2026-09-11T21:13:38Z https://fordatis.fraunhofer.de/handle/fordatis/289 Titel: Truck Component Simulation Meshes and Synthetic LiDAR Measurements Datenautorinnen und Datenautoren: Garcke, Jochen; Hahner, Sara; Iza-Teran, Rodrigo Zusammenfassung: This is the synthetic dataset from the paper: Jochen Garcke, Sara Hahner, and Rodrigo Iza-Teran. "Alignment of Highly Resolved Time-Dependent Experimental and Simulated Crash Test Data". In: International Journal of Crashworthiness. The work compares highly resolved experimental data with corresponding simulation data. The dataset contains one component from several completed frontal crash simulations of a Chevrolet C2500 pick-up truck model (from National Crash Analysis Center (NCAC)). Additionally, we provide our LiDAR measurements of the component. The data, which is provided here, is described in detail in section 5.3 of the paper. 2022-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/262.3 Titel: Wind turbine gearbox simulation data Datenautorinnen und Datenautoren: Climaco, Paolo; Garcke, Jochen; Iza Teran, Victor Rodrigo; Lecei, Ivan Zusammenfassung: This dataset consists of simulated acceleration signals representing the vibration response of a generic geared-rotor bearing system made of a rigid gearbox with a spur gear pair mounted on flexible shafts supported by rolling element bearings. The signals include a stochastic component simulating the varying load condition caused by wind turbulence. The data have been generated simulating two distinct gearbox health scenarios and two wind speed conditions. Specifically, signals represent a situation in which a perfectly healthy gearbox is modelled or a scenario in which one of the gears is damaged. The simulated damage consists of a cracked tooth in one of the two gears. Moreover, independently of the gearbox's health condition, we consider the two wind speed scenarios of 5 m/s and 13m/s. Further information on the data can be found in the 'Readme.md' file. 2022-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/267 Titel: FAST Wind Turbine simulations for mass and aerodynamic imbalance detection Datenautorinnen und Datenautoren: Lecei, Ivan Zusammenfassung: The dataset was created in order to be able to train machine learning models in the domain of wind energy, especially for the use cases of predictive maintenance / condition monitoring and anomaly detection. In the wind energy domain, one usually suffers from two main problems: Firstly, the lack of sufficiently many labeled data instances, especially for rarely occurring anomalous states. Secondly, the stochastic nature of the resulting measurements, as changing and turbulent environmental conditions lead to a lot of noise in the recorded sensor signals, which in turn makes the analysis of the data very hard. To this end, a synthetic wind turbine dataset has been created, which addresses these issues. The dataset allows to obtain a more controlled view than measurements in the field would yield, since for different fixed conditions of the wind turbine, simulations have been carried out for a various number of environmental conditions. The goal is to be able to learn data representations for different states of the wind turbine from the simulated data, which would not be possible on real measurements. Moreover, as the simulations can be carried out with a very high number of different sensor outputs, the dataset offers a possibility to uncover relations between different measurements / components of the wind turbine, which are not known yet. The dataset consists strictly of highdimensional time series as output of certain setups of a 5MW wind turbine. They represent various measurements of important characteristics, such as acceleration or bending moments, on the blades, the nacelle, or different parts of the wind turbine. The dataset is created to resemble different locations and degrees of mass imbalance on one of the three wind turbine blades. 2022-01-01T00:00:00Z