The dynamics of winding systems in roll-to-roll processes critically influence the roll quality. As direct measurement of web parameters near the winder and rewinder is often impractical due to cost and complexity, the state of the roll remains unknown. To overcome this challenge, this paper presents a novel condition monitoring methodology that estimates variations in web roll parameters such as thickness, roll density, and inertia. This eventually facilitates the computation of internal stresses in the web roll. This methodology integrates physics-based modeling with real-time data, which is made possible with a digital twin platform, thus enabling precise quality tracking in real time. Compared to conventional monitoring techniques, the proposed approach reduces reliance on physical sensors and provides a scope for the user to utilize real-time feedback to integrate a product-level control. Experimental validation demonstrates the effectiveness of the methodology by identifying roll quality and hence provides scope for optimizing the process control.

Accurate modeling of mechatronic motion systems requires accounting for nonlinear load torque effects arising from mechanical and electromagnetic interactions. This paper presents a non-invasive methodology to characterize such effects through spatial frequency analysis of electromagnetic motor torque signals collected during constant velocity experiments. Using the Nonuniform Fast Fourier Transform (NUFFT), the torque signal is analyzed in the spatial domain without requiring uniform positional sampling. The load torque is decomposed according to a proposed formulation into dominant periodic features and a nonlinear friction profile, enabling interpretable, position-dependent insights into system behavior. The approach is demonstrated on a linear tooth belt-driven system, revealing periodic components related to 1) belt-to-pulley tooth engagement, 2) motor cogging torque, and 3) additional motor-induced oscillations. These features are consistently identified across a range of velocities, without requiring additional sensors or excitation signals. The resulting decomposition enhances understanding of individual torque contributions and supports model-based control, digital twin implementation, condition monitoring, and compensation strategies in mechatronic systems.

In the context of web processing machines, the radial geometry of rolls or bobbins, the input material to the machine, must be measured with precision. The control system of the machine requires a measurement of the varying dimension of the bobbins as an input. A web processing machine (WPM) utilizes the rotational speed to regulate the tension of the web within the machine. Consequently, precise radius measurements, for optimal tension control, are of paramount importance. Traditional sensors, e.g., infrared and ultrasonic sensors, lack precision and are unable to detect anomalies. Additionally, their radius measurement is indirect, as these devices essentially function as distance sensors, and thus necessitate a calibration process. Furthermore, as these sensors only measure a single point on the bobbin, the system is vulnerable to localized irregularities and disturbances that could compromise the integrity of the material. This work proposes a novel methodology for measuring the radius of bobbins. A camera system is used in conjunction with Frequency Domain Zero Padding (FDZP) as a sub-pixel technique to ensure high precision. A correction is applied to compensate for the perspective-induced distortion resulting from the camera’s radial viewpoint with respect to the center of the roll. Moreover, the camera possesses the capability to evaluate multiple points on the bobbin, which enables the detection of the aforementioned anomalies at different positions on the bobbin. The system’s validation was conducted in an industrial setting, where it demonstrated a precision that is 6.2 times better than that of conventional sensors. The method ensures real-time performance while remaining low-cost.

The stability assessment of construction vehicles, which are part of a constantly growing market, is of a high importance for safety and working efficiency. For such vehicles, the stability is mainly impacted by the carried payload. In this paper, a state of the art payload estimation method, based on simplified motion equations, is further improved by coupling it with accurate real-time multibody modelling. An example, that allows to reduce the important impact of joints damping on the payload estimation method, is developed and validated in this paper. A reduction of the payload estimation moving window root mean square error from 12.8% to 2.9% is obtained. Finally, the tractor multibody digital-twin is integrated in a real-time system on a physical setup, allowing to process the signals of the tractor and provide an easy to interpret visualization of the vehicle stability to the operator.

Wind turbines are one of the main sources of renewable energy worldwide. Although large wind turbines in the MW range are popular, small wind turbines of several kW can also have a valuable contribution since they can be installed in a more decentralized manner. However, the energy yield of these small wind turbines still needs improvement. The performance of the Maximum Power Point Tracker (MPPT) has a crucial impact on the energy yield. In order to design and set-up the MPPT, knowledge of the Cp(λ) curve of the turbine blades has an important added value. However, this curve is often not known as it requires a sufficiently large wind tunnel or complex computational fluid dynamics calculations. In this paper, a new alternative method is presented to estimate the Cp(λ) curve by measuring the wind speed, generator voltage and current. This method can be applied on any turbine, small or large, during normal operation and does not require an intervention in the turbine itself, e.g., for placement of additional sensors. The Cp(λ) estimation method is implemented on a lab-scale wind turbine emulator to obtain experimental validation.