TY - CONF AU - Mattei, Annalisa AU - Eremin, Oxana ID - 45326 T2 - Bericht zur Jahrestagung der Sektion Frauen- und Geschlechterforschung in der Erziehungswissenschaft in der Deutschen Gesellschaft für Erziehungswissenschaft TI - Corona und Krise. Perspektiven erziehungswissenschaftlicher Frauen- und Geschlechterforschung ER - TY - CHAP AU - Schemmer, Susanne Jutta AU - Heisler, Dietmar AU - Rink, Julia ED - Weyland, Ulrike ED - Ziegler, Birgit ED - Driesel-Lange, Katja ED - Kruse, Annika ID - 45319 T2 - Entwicklungen und Perspektiven in der Berufsorientierung. Stand und Herausforderungen. Online: (06.12.2021) TI - Entwicklungschance oder Warteschleife? Berufsorientierung und Berufswahl in der Berufsfachschule ER - TY - CONF AU - Kenter, Tobias AU - Shambhu, Adesh AU - Faghih-Naini, Sara AU - Aizinger, Vadym ID - 46194 T2 - Proceedings of the Platform for Advanced Scientific Computing Conference TI - Algorithm-hardware co-design of a discontinuous Galerkin shallow-water model for a dataflow architecture on FPGA ER - TY - BOOK AB - Trotz massiver Förderungen für die Digitalisierung ist die Präsenzlehre noch immer der Standard an deutschen Hochschulen. Aufgrund des Physical Distancing im Zuge der Corona-Pandemie musste sie jedoch kurzfristig fast vollständig digitalisiert werden. Die Beiträge des Bandes bieten einen multiperspektivischen Zugang zu den damit verbundenen Herausforderungen und beleuchten, wie die verschiedenen Akteur*innen die Umstellung auf digitale Lehr- und Lernformate umgesetzt und erlebt haben. Durch die Zusammenführung der verschiedenen Sichtweisen können die Bedarfe und Wünsche der einzelnen Akteursgruppen zusammengebracht und bei der nachhaltigen Weiterentwicklung der Hochschullehre besser berücksichtigt werden. ED - Neiske, Iris ED - Osthushenrich, Judith ED - Schaper, Niclas ED - Trier, Ulrike ED - Vöing, Nerea ID - 46613 SN - 2749-7623 TI - Hochschule auf Abstand ER - TY - CONF AB - Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results. AU - Bender, Amelie AU - Sextro, Walter ED - Do, Phuc ED - King, Steve ED - Fink, Olga ID - 22724 IS - 1 KW - Hybrid prediction method KW - Multi-model particle filtering KW - Uncertainty quantification KW - RUL estimation T2 - Proceedings of the European Conference of the PHM Society 2021 TI - Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties VL - 6 ER - TY - CONF AB - Several methods, including order analysis, wavelet analysis and empirical mode decomposition have been proposed and successfully employed for the health state estimation of technical systems operating under varying conditions. However, where information such as the speed of rotating machinery, component specifications or other domain-specific information is unavailable, such methods are often infeasible. Thus, this paper investigates the application of classical time-domain features, features from the medical field and novel features from the highly comparative time-series analysis (HCTSA) package, for the health state estimation of rotating machinery operating under varying conditions. Furthermore, several feature selection methods are investigated to identify features as viable health indicators for the diagnostics and prognostics of technical systems. As a case study, the presented methods are evaluated on real-world and experimentally acquired vibration data of bearings operating under varying speed. The results show that the selected features can successfully be employed as health indicators for technical systems operating under varying conditions. AU - Aimiyekagbon, Osarenren Kennedy AU - Bender, Amelie AU - Sextro, Walter ID - 22507 KW - Wind turbine diagnostics KW - bearing diagnostics KW - non-stationary operating conditions KW - varying operating conditions KW - feature extraction KW - feature selection KW - fault detection KW - failure detection T2 - Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021) TI - On the applicability of time series features as health indicators for technical systems operating under varying conditions ER - TY - CONF AB - In the industry 4.0 era, there is a growing need to transform unstructured data acquired by a multitude of sources into information and subsequently into knowledge to improve the quality of manufactured products, to boost production, for predictive maintenance, etc. Data-driven approaches, such as machine learning techniques, are typically employed to model the underlying relationship from data. However, an increase in model accuracy with state-of-the-art methods, such as deep convolutional neural networks, results in less interpretability and transparency. Due to the ease of implementation, interpretation and transparency to both domain experts and non-experts, a rule-based method is proposed in this paper, for prognostics and health management (PHM) and specifically for diagnostics. The proposed method utilizes the most relevant sensor signals acquired via feature extraction and selection techniques and expert knowledge. As a case study, the presented method is evaluated on data from a real-world quality control set-up provided by the European prognostics and health management society (PHME) at the conference’s 2021 data challenge. With the proposed method, our team took the third place, capable of successfully diagnosing different fault modes, irrespective of varying conditions. AU - Aimiyekagbon, Osarenren Kennedy AU - Muth, Lars AU - Wohlleben, Meike Claudia AU - Bender, Amelie AU - Sextro, Walter ED - Do, Phuc ED - King, Steve ED - Fink, Olga ID - 27111 IS - 1 KW - PHME 2021 KW - Feature Selection Classification KW - Feature Selection Clustering KW - Interpretable Model KW - Transparent Model KW - Industry 4.0 KW - Real-World Diagnostics KW - Quality Control KW - Predictive Maintenance T2 - Proceedings of the European Conference of the PHM Society 2021 TI - Rule-based Diagnostics of a Production Line VL - 6 ER - TY - JOUR AU - Alhaddad, Samer AU - Förstner, Jens AU - Groth, Stefan AU - Grünewald, Daniel AU - Grynko, Yevgen AU - Hannig, Frank AU - Kenter, Tobias AU - Pfreundt, Franz‐Josef AU - Plessl, Christian AU - Schotte, Merlind AU - Steinke, Thomas AU - Teich, Jürgen AU - Weiser, Martin AU - Wende, Florian ID - 24788 JF - Concurrency and Computation: Practice and Experience KW - tet_topic_hpc SN - 1532-0626 TI - The HighPerMeshes framework for numerical algorithms on unstructured grids ER - TY - GEN AU - Lammer, Christina ID - 30961 TI - Andrea Karimé: Sterne im Kopf und ein unglaublicher Plan. Köln: Peter Hammer Verlag 2021 ER - TY - JOUR AB - AbstractQuantum well (QW) heterostructures have been extensively used for the realization of a wide range of optical and electronic devices. Exploiting their potential for further improvement and development requires a fundamental understanding of their electronic structure. So far, the most commonly used experimental techniques for this purpose have been all-optical spectroscopy methods that, however, are generally averaging in momentum space. Additional information can be gained by angle-resolved photoelectron spectroscopy (ARPES), which measures the electronic structure with momentum resolution. Here we report on the use of extremely low-energy ARPES (photon energy ~ 7 eV) to increase depth sensitivity and access buried QW states, located at 3 nm and 6 nm below the surface of cubic-GaN/AlN and GaAs/AlGaAs heterostructures, respectively. We find that the QW states in cubic-GaN/AlN can indeed be observed, but not their energy dispersion, because of the high surface roughness. The GaAs/AlGaAs QW states, on the other hand, are buried too deep to be detected by extremely low-energy ARPES. Since the sample surface is much flatter, the ARPES spectra of the GaAs/AlGaAs show distinct features in momentum space, which can be reconducted to the band structure of the topmost surface layer of the QW structure. Our results provide important information about the samples’ properties required to perform extremely low-energy ARPES experiments on electronic states buried in semiconductor heterostructures. AU - Hajlaoui, Mahdi AU - Ponzoni, Stefano AU - Deppe, Michael AU - Henksmeier, Tobias AU - As, Donat Josef AU - Reuter, Dirk AU - Zentgraf, Thomas AU - Springholz, Gunther AU - Schneider, Claus Michael AU - Cramm, Stefan AU - Cinchetti, Mirko ID - 25227 JF - Scientific Reports SN - 2045-2322 TI - Extremely low-energy ARPES of quantum well states in cubic-GaN/AlN and GaAs/AlGaAs heterostructures VL - 11 ER -