Universität Stuttgart
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Item Open Access An industrial case study on the evaluation of a safety engineering approach for software-intensive systems in the automotive domain(2016) Abdulkhaleq, Asim; Vöst, Sebastian; Wagner, Stefan; Thomas, JohnSafety remains one of the essential and vital aspects in today's automotive systems. These systems, however, become ever more complex and dependent on software which is responsible for most of their critical functions. Therefore, the software components need to be analysed and verified appropriately in the context of software safety. The complexity of software systems makes defining software safety requirements with traditional safety analysis techniques difficult. A new technique called STPA (Systems-Theoretic Process Analysis) based on system and control theory has been developed by Leveson to cope with complex systems. Based on STPA, we have developed a comprehensive software safety engineering approach in which the software and safety engineers integrate the analysis of software risks with their verification to recognize the software-related hazards and reduce the risks to a low level. In this paper, we explore and evaluate the application of our approach to a real industrial system in the automotive domain. The case study was conducted analysing the software controller of the Active Cruise Control System (ACC) of the BMW Group.Item Open Access A microstructurally-based, multi-scale, continuum-mechanical model of skeletal muscle tissue(2019) Bleiler, Christian; Ponte Castañeda, Pedro; Röhrle, OliverItem Open Access Deep learning based soft sensors for industrial machinery(2020) Maschler, Benjamin; Ganssloser, Sören; Hablizel, Andreas; Weyrich, MichaelA multitude of high quality, high-resolution data is a cornerstone of the digital services associated with Industry 4.0. However, a great fraction of industrial machinery in use today features only a bare minimum of sensors and retrofitting new ones is expensive if possible at all. Instead, already existing sensors’ data streams could be utilized to virtually ‘measure’ new parameters. In this paper, a deep learning based virtual sensor for estimating a combustion parameter on a large gas engine using only the rotational speed as input is developed and evaluated. The evaluation focusses on the influence of data preprocessing compared to network type and structure regarding the estimation quality.Item Open Access Symplectic model order reduction with non-orthonormal bases(2019) Buchfink, Patrick; Bhatt, Ashish; Haasdonk, BernardParametric high-fidelity simulations are of interest for a wide range of applications. But the restriction of computational resources renders such models to be inapplicable in a real-time context or in multi-query scenarios. Model order reduction (MOR) is used to tackle this issue. Recently, MOR is extended to preserve specific structures of the model throughout the reduction, e.g. structure-preserving MOR for Hamiltonian systems. This is referred to as symplectic MOR. It is based on the classical projection-based MOR and uses a symplectic reduced order basis (ROB). Such a ROB can be derived in a data-driven manner with the Proper Symplectic Decomposition (PSD) in the form of a minimization problem. Due to the strong nonlinearity of the minimization problem, it is unclear how to efficiently find a global optimum. In our paper, we show that current solution procedures almost exclusively yield suboptimal solutions by restricting to orthonormal ROBs. As new methodological contribution, we propose a new method which eliminates this restriction by generating non-orthonormal ROBs. In the numerical experiments, we examine the different techniques for a classical linear elasticity problem and observe that the non-orthonormal technique proposed in this paper shows superior results with respect to the error introduced by the reduction.Item Open Access User-friendly, requirement-based assistance for production workforce using an asset administration shell design(2020) Al Assadi, Anwar; Fries, Christian; Fechter, Manuel; Maschler, Benjamin; Ewert, Daniel; Schnauffer, Hans-Georg; Zürn, Michael; Reichenbach, MatthiasFuture production methods like cyber physical production systems (CPPS), flexibly linked assembly structures and the matrix production are characterized by highly flexible and reconfigurable cyber physical work cells. This leads to frequent job changes and shifting work environments. The resulting complexity within production increases the risk of process failures and therefore requires longer job qualification times for workers, challenging the overall efficiency of production. During operation, cyber physical work cells generate data, which are specific to the individual process and worker. Based on the asset administration shell for Industry 4.0, this paper develops an administration shell for the production workforce, which contains personal data (e.g. qualification level, language skills, machine access, preferred display and interaction settings). Using worker and process specific data as well as personal data, allows supporting, training and instating workers according to their individual capabilities. This matching of machine requirements and worker skills serves to optimize the allocation of workers to workstations regarding the ergonomic workplace setup and the machine efficiency. This paper concludes with a user-friendly, intuitive design approach for a personalized machine user interface. The presented use-cases are developed and tested at the ARENA2036 (Active Research Environment for the Next Generation of Automobiles) research campus.Item Open Access Analysis of the SPH interpolation moments matrix with regard to the influences of the discretization error in adaptive simulations(2021) Heinzelmann, Pascal; Spreng, Fabian; Sollich, Daniel; Eberhard, Peter; Williams, John R.Item Open Access Effiziente Modellierung flexibler Robotersysteme zur Echtzeitsimulation am Beispiel eines Leichtbauroboters(2025) Hoschek, Sebastian; Rodegast, Philipp; Gesell, Jakob; Scheid, Jonas; Fehr, JörgDie Echtzeitsimulation mechanischer Systeme und deren digitale Zwillinge gewinnen in der Industrie zunehmend an Bedeutung. Sie ermöglichen unter anderem die Optimierung von Steuerungsalgorithmen, die Vorhersage des Systemverhaltens und die Implementierung von Regelstrategien in der Automatisierungstechnik. Ein Industriepartner entwickelt derzeit einen mobilen Leichtbauroboter für den Einsatz im Logistikbereich, bei dem die hohe Flexibilität der Struktur zu elastischen Durchbiegungen führt. Um die Genauigkeit und Leistungsfähigkeit des Roboters zu verbessern, ist eine präzise Modellierung dieser elastischen Effekte erforderlich. In dieser Arbeit werden zwei verschiedene Modellierungsansätze für die Echtzeitsimulation untersucht. Der erste basiert auf einer physikalischen White-Box-Modellierung als flexibles Mehrkörpersystem, wobei ein klassisches Finite-Elemente-Modell (FEM) durch Modellordnungsreduktion vereinfacht wird, um eine effiziente Berechnung zu ermöglichen. Der zweite Ansatz verwendet ein Finite-Segmente-Modell, das eine Parameteridentifikation erfordert, um eine realitätsgetreue Abbildung des Systemverhaltens zu gewährleisten. Beide Methoden werden auf den Leichtbauroboter angewendet und hinsichtlich ihrer Vor- und Nachteile verglichen. Wesentliche Kriterien sind dabei der Modellierungsaufwand, die Berechnungsgeschwindigkeit und die Genauigkeit der Simulationsergebnisse. Die Ergebnisse liefern eine Entscheidungsgrundlage zur Auswahl geeigneter Modellierungsmethoden in Echtzeitanwendungen.Item Open Access The size of the functional base of support decreases with age(2025) Sloot, Lizeth H.; Gerhardy, Thomas; Mombaur, Katja; Millard, MatthewFalls occur more often as we age. To identify people at risk of falling, balance analysis requires an accurate base-of-support model. We previously developed a functional base-of-support (fBOS) model for standing young adults and showed that its area is smaller than the footprint area. Our fBOS model is a polygon that contains centre-of-pressure trajectories recorded as standing participants move their bodies in the largest possible loop while keeping their feet flat on the ground. Here we assess how the size of the fBOS area changes with age by comparing 38 younger (YA), 14 middle-aged (MA), and 34 older adults (OA). The fBOS area is smaller in older adults: OA area is 58% of the YA area (p<0.001), and 59% of the MA area (p=0.001), with no difference between YA and MA. The reduction in fBOS area among the OA is primarily caused by a reduction in the length of the fBOS. In addition, among older adults smaller fBOS areas correlated with a lower score on the Short Physical Performance Battery (τ=0.28, p=0.04), a reduced walking speed (τ=0.25, p=0.04), and a higher frailty level (p=0.09). So that others can extend our work, we have made our fBOS models available online.Item Open Access A polygon model of the functional base-of-support improves the accuracy of balance analysis(2025) Millard, Matthew; Sloot, Lizeth H.Mathematical balance models have the potential to identify people at risk of falling before an injury occurs. However, most balance models depend on a model of the base-of-support (BOS) of the feet to calculate how well someone is balancing. Here we evaluate the functional base-of-support (fBOS): the convex polygon on the bottom of the foot that can support a large fraction of the body's weight. First, we develop a geometric model of the fBOS by measuring the center-of-pressure (COP) and kinematic data of the feet of 27 younger adults instructed to move their body mass in large loops without taking a step. We extract a planar convex polygon that contains the COP data. Finally, we compare the area of this fBOS model to a marker-based BOS model before evaluating if the fBOS differs across four everyday conditions: footwear, stance-width, foot dominance, and during single and double stance. We found that the fBOS is much smaller (23% the size) than a marker-based BOS model. Our analysis suggests that using the fBOS, rather than a marker-based BOS, can improve the accuracy of the margin-of-stability by 20% of foot width and 16% of the length. In addition, we found that the fBOS area does not differ across footwear (p=0.88), stance-width (p=0.88), and foot dominance (p=0.68), but during single stance the fBOS is 17% (p=0.0003) larger than during double stance. So that others can use and extend our work, we have made the models, example data and code publicly available.