Open Access

Torsionsschwingungen von Radsätzen mit torsionssteif gekoppelten Radkörpern können beträchtliche Torsionsmomente in der Radsatzwelle von Schienenfahrzeugen verursachen. Die Anregung der Schwingung erfolgt durch einen mit zunehmender Gleitgeschwindigkeit abfallenden Kraftschluss im Rad-Schiene-Kontakt. Messergebnisse zeigen, dass die entstehenden dynamischen Torsionsmomente in der Radsatzwelle ein Vielfaches des quasistatischen Nennmoments betragen können. Die vorliegende Arbeit beschreibt verschiedene Berechnungsverfahren die realistische, maximale, dynamische Torsionsmomente der Radsatzwelle ermitteln. Außerdem werden Konstruktionspotentiale identifiziert, die das dynamische Torsionsmaximum reduzieren. Auf Grundlage der Rad-Schiene-Kontaktmechanik und der Analyse von Messdaten werden einerseits kritische, schwingungsanregende Kraftschlussfunktionen ermittelt und andererseits die relevanten Eigenfrequenzen der Antriebssysteme festgestellt. Die modellierten Mehrkörpersysteme ermöglichen durch Modalanalysen ein tieferes Verständnis der Schwingungssysteme im Hinblick auf die zu untersuchende Radsatz-Torsions-schwingung. Parameterstudien und Stabilitätsuntersuchungen zeigen die Einflüsse auf die Dämpfung und des damit verbundenen dynamischen Torsionsmoments, wodurch mehrere Optimierungsmaß-nahmen aufgezeigt werden können. Der entwickelte Berechnungsansatz führt schließlich durch gezielte Vereinfachungen auf ein analytisches Verfahren, welches im Vergleich zu den numerischen Berechnungen akzeptable Ergebnisse hinsichtlich des dynamischen Torsionsmaximums erreicht und in der Vorauslegung von Radsätzen und Antriebssystemen verwendet werden kann.

Digital transformation urges organizations to strategically invest in information technology (IT) to keep up with the competition. The responsible strive to choose the right digital initiatives that can maximize the benefit. Thereby, they still struggle to communicate IT costs and demonstrate the business value of IT. The goal of this paper is to get a deeper understanding of the perception of IT costs and business value and support their effective communication. Applying the focus group method, we analyzed in four interview sessions that stakeholders perceive IT costs and business value differently and that a common perception serves as the basis of communication. We then identified and evaluated 20 success factors to establish effective communication of IT costs and IT business value. Hence, this paper enables a better understanding of the perception and the operationalization of effective communication mainly between business and IT executives regarding IT costs and IT business value.

The problem of controlling autonomous surface vessels in an energy-optimal way is important for the electrification of maritime systems and is currently being investigated by many researchers. In this paper, we use numerical optimal control to plan an energy-optimal docking trajectory in river currents and show that it can save energy compared to other widespread planning approaches. An optimal control problem including a detailed vessel model is defined, transcribed into a nonlinear optimization problem via direct multiple shooting, and solved using a homotopy procedure. The optimal solution is compared to a geometrical path planning approach with path-velocity decomposition. The results of this comparison show that prescribing a path with fixed vessel orientation leads to very suboptimal results. Further, we demonstrate how shrinking horizon MPC can control the vessel in an energy-optimal way even under severe disturbances, by replanning the energy-optimal trajectories in real-time. We believe that energy-optimal MPC could become a key technology for the electrification of maritime systems.

One-class incremental learning is a special case of class-incremental learning, where only a single novel class is incrementally added to an existing classifier instead of multiple classes. This case is relevant in industrial defect detection scenarios, where novel defects usually appear during operation. Existing rolled-out classifiers must be updated incrementally in this scenario with only a few novel examples. In addition, it is often required that the base classifier must not be altered due to approval and warranty restrictions. While simple finetuning often gives the best performance across old and new classes, it comes with the drawback of potentially losing performance on the base classes (catastrophic forgetting [1]). Simple prototype approaches [2] work without changing existing weights and perform very well when the classes are well separated but fail dramatically when not. In theory, null-space training (NSCL) [3] should retain the basis classifier entirely, as parameter updates are restricted to the null space of the network with respect to existing classes. However, as we show, this technique promotes overfitting in the case of one-class incremental learning. In our experiments, we found that unconstrained weight growth in null space is the underlying issue, leading us to propose a regularization term (R-NSCL) that penalizes the magnitude of amplification. The regularization term is added to the standard classification loss and stabilizes null-space training in the one-class scenario by counteracting overfitting. We test the method’s capabilities on two industrial datasets, namely AITEX and MVTec, and compare the performance to state-of-the-art algorithms for class-incremental learning.