Browsing by Author "Hantschke, Peter"

Now showing 1 - 5 of 5

Open Access
3-component servo-hydraulic test bench measurements for virtual sensing and forward prediction tasks
(Technische Universität Dresden, 2021-12-17) Heindel, Leonhard; Hantschke, Peter; Kästner, Markus
This dataset was created to provide measurements of a non-linear dynamic system with multiple input and output signal channels for the developement and testing of virtual sensing and forward prediction algorithms. It also includes predictions of a frequency response function model, which can be used as a benchmark for comparison with novel algorithmic approaches. The dataset contains measurements from a three-component servo-hydraulic fatigue test bench for suspension hydro-mounts. The sensor setup of this test bench consists of 3 inertia compensated force and 3 displacement sensors. Various non-linear influences affect the system behavior. The hydro-mounts are filled with oil to provide highly non-linear dampening, while the pendulum kinematics of the test bench introduce non-linear interactions between the excitations in different spatial directions. The most impactful non-linearity results from the system stiffness.
Open Access
eBike measurements for fatigue monitoring and maneuver identification tasks
(Technische Universität Dresden, 2022-09-26) Heindel, Leonhard; Hantschke, Peter; Kästner, Markus
This dataset provides acceleration and strain measurements from a sensor equipped eBike, which were collected for the development of new methods for fatigue damage monitoring and maneuver identification tasks.
Open Access
eBike measurements for fatigue monitoring using acceleration differences
(Technische Universität Dresden, 2025-06-26) Heindel, Leonhard; Hantschke, Peter; Kästner, Markus
In fatigue monitoring, the aim is to approximate the fatigue damage accumulation in a system, for example to schedule maintenance intelligently. Virtual sensing can be deployed to obtain the required information more efficiently by estimating them from readily available sensor data. This dataset contains acceleration and strain measurements from a sensor equipped eBike, designed to develop such methods. Using the acceleration data, the aim is to estimate the fatigue damage accumulation at the strain gauge positions. Here, the sensors are positioned such that differences between multiple acceleration sensors provide information that is related to the deformation of the eBike frame, so that a connection to strain gauge data can be established by computing differences between integrated displacement data. The dataset includes data intended for training and testing of data-driven modeling approaches, which is obtained from regular eBike usage. Additional labeled data enables the analysis of specific riding maneuvers.
Open Access
Experimental characterization of fatigue behavior of 42CrMo4+QT material specimens and cross-rolled components
(Technische Universität Dresden, 2023-08-29) Schneider, Tom; Ellmer, Franz; Guilleaume, Christina; Hantschke, Peter; Brosius, Alexander; Kästner, Markus
This dataset provides a cyclic material characterization from cyclic tests of material specimen made of 42CrMo4+QT. In addition, fatigue life data are included for cross-rolled components produced with different process variants at different load levels and loadings.
Open Access
Predictive maintenance demonstrator dataset with individual load histories
(Technische Universität Dresden, 2025-04-14) Heindel, Leonhard; Hantschke, Peter; Kästner, Markus
Predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. Virtual sensing methods predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. This dataset provides a demonstrator example where failure occurs based on individual load histories. The sensor setup consists of simple notched steel specimens, which are clamped between two servo-hydraulic cylinders of a fatigue test bench. It is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes.