Browsing by Author "Heindel, Leonhard"

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  • ItemOpen 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.
  • ItemOpen 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.