Universität Stuttgart
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Item Open Access Nachhaltigkeit in der Energieversorgung - relevante Stromerzeugungstechniken auf dem Prüfstand(1998) Voß, AlfredDas Leitbild einer "Nachhaltigen Entwicklung" (sustainable development) hat in den letzten Jahren eine erstaunliche Karriere gemacht. Seit der Konferenz der Vereinten Nationen über Umwelt und Entwicklung (UNCED) in Rio de Janeiro 1992 ist das Ziel einer nachhaltigen Entwicklung das zentrale Leitbild der internationalen umwelt-, wirtschafts- und entwicklungspolitischen Diskussion, das wirtschaftliche Entwicklung zur Überwindung von Hunger und Armut, und die Schaffung humaner Lebensbedingungen sowie den Erhalt der natürlichen Lebensgrundlagen miteinander verbinden will. Auch in die energiepolitische und energiewirtschaftliche Diskussion hat das Leitbild einer nachhaltigen Entwicklung mittlerweile verstärkt Eingang gefunden. Obwohl festzustellen ist, daß das Leitbild einer nachhaltigen Entwicklung auch über die verschiedenen gesellschaftlichen Gruppen hinweg eine breite prinzipielle Zustimmung findet, so spannen doch die Vorstellungen und Interpretationen des Leitbildes, sowohl hinsichtlich ihrer normativen bzw. theoretisch-naturwissenschaftlichen Fundierung als auch hinsichtlich ihrer abgeleiteten Handlungsziele bzw. Handlungsanweisungen - dies gilt gerade für den Energiebereich - eine große Bandbreite auf. Dies birgt nicht nur die Gefahr, daß dieses Leitbild von verschiedenen Interessengruppen instrumentalisiert wird, sondern auch, daß falsche Weichenstellungen vorgenommen werden. Aus diesem Grund erscheint es notwendig, auch wegen der essentiellen Bedeutung, die der Energieversorgung für eine nachhaltige Entwicklung zukommt, sich über die Konkretisierung des Leitbildes zu verständigen, um die Energieversorgungsoptionen, aber auch die energiepolitischen Vorstellungen, diesbezüglich einordnen zu können.Item Open Access ERA - Energy-based reliability analysis - Energiebasierte Zuverlässigkeitsanalyse(2014) Kemmler, Stefan; Koller, Oliver; Bertsche, BerndDa die Wechselwirkungen zwischen mechatronischen Komponenten in Systemen eine entscheidende Rolle auf ihre Belastung einnehmen, ist die Betrachtung dieser Wechselwirkungen un- verzichtbar. Zur Identifikation solcher Wechselwirkungen ist eine ergänzende Methode zur den bisher klassischen Systemanalysen von Nöten. Dies wird bei der vorgestellten energiebasierten Zuverlässigkeitsanalyse (engl. Energy-based Reliability Analysis - ERA) berücksichtigt, indem die stationären Energie- beziehungsweise die dynamischen Leistungsflüsse mechatronischer Systemen in Form von Energieflussdiagrammen dargestellt werden. Mit der Modellierung des Energieflusses und damit das Ansetzen des ERA-Verfahrens kann der Nutzer Wirkzusammenhänge und Schwachstellen erkennen, eine exaktere Bestimmung der Zuverlässigkeit durch Berechnung der Belastung erreichen und folglich Komponenten zuverlässigkeitsbasiert auslegen.Item Open Access Distributed cooperative deep transfer learning for industrial image recognition(2020) Maschler, Benjamin; Kamm, Simon; Nasser, Jazdi; Weyrich, MichaelIn this paper, a novel light-weight incremental class learning algorithm for live image recognition is presented. It features a dual memory architecture and is capable of learning formerly unknown classes as well as conducting its learning across multiple instances at multiple locations without storing any images. In addition to tests on the ImageNet dataset, a prototype based upon a Raspberry Pi and a webcam is used for further evaluation: The proposed algorithm successfully allows for the performant execution of image classification tasks while learning new classes at several sites simultaneously, thereby enabling its application to various industry use cases, e.g. predictive maintenance or self-optimization.Item Open Access A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning(2020) Kneifl, Jonas; Grunert, Dennis; Fehr, JörgThe paper uses a nonlinear non-intrusive model reduction approach, to derive efficient and accurate surrogate models for structural dynamical problems. Therefore, a combination of proper orthogonal decomposition along with regression algorithms from the field of machine learning is utilized to capture the dynamics in a reduced representation. This allows highly performant approximations of the original system. In this context, we provide a comparison of several regression algorithms based on crash simulations of a structural dynamic frame.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 On data-based estimation of possibility distributions(2019) Hose, Dominik; Hanss, MichaelIn this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a very general approach to possibilistic identification problems in the framework of imprecise probabilities, i.e. the non-parametric estimation of possibility distributions of uncertain variables from data with a clear interpretation.Item Open Access Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems(2019) Grunert, Dennis; Fehr, Jörg; Haasdonk, BernardModel Order Reduction is used to vastly speed up simulations but it also introduces an error to the simulation results, which needs to be controlled. The performance of the general to use, a-posteriori error estimator of Ruiner et al. for second-order systems is analyzed and a bottleneck is found in the offline stage making it unusable for larger models. We use the spectral theorem, power series expansions, monotonicity properties, and self-tailored algorithms to speed up the offline stage largely by one polynomial order both in terms of computation time as well as storage complexity. All properties are proven rigorously. This eliminates the aforementioned bottleneck. Hence, the error estimator of Ruiner et al. can finally be used for large, linear, second-order mechanical systems reduced by any model reduction method based on Petrov-Galerkin reduction. The examples show speedups of up to 28.000 and the ability to compute much larger systems with a fixed amount of memory.Item 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 Realization of AI-enhanced industrial automation systems using intelligent Digital Twins(2020) Nasser, Jazdi; Ashtari Talkhestani, Behrang; Maschler, Benjamin; Weyrich, MichaelA requirement of future industrial automation systems is the application of intelligence in the context of their optimization, adaptation and reconfiguration. This paper begins with an introduction of the definition of (artificial) intelligence to derive a framework for artificial intelligence enhanced industrial automation systems: An artificial intelligence component is connected with the industrial automation system’s control unit and other entities through a series of standardized interfaces for data and information exchange. This framework is then put into context of the intelligent Digital Twin architecture, highlight the latter as a possible implementation of such systems. Concluding, a prototypical implementation on the basis of a modular cyber-physical production system is described. The intelligent Digital Twin realized this way provides the four fundamental sub-processes of intelligence, namely observation, analysis, reasoning and action. A detailed description of all technologies used is given.Item Open Access On the solution of forward and inverse problems in possibilistic uncertainty quantification for dynamical systems(2020) Hose, Dominik; Hanss, MichaelIn this contribution, we adress an apparent lack of methods for the robust analysis of dynamical systems when neither a precise statistical nor an entirely epistemic description of the present uncertainties is possible. Relying on recent results of possibilistic calculus, we revisit standard prediction and filtering problems and show how these may be solved in a numerically exact way.