Zustandsüberwachung zur Prognose der Restlebensdauer von Gummi-Metall-Elementen unter Berücksichtigung systembasierter Unsicherheiten - Bender, Amelie
Eine zustandsbasierte Instandhaltungsstrategie reduziert das Risiko eines Ausfalls eines technischen Systems bei gleichzeitig hoher Ausnutzung und planbaren Instandhaltungsmaßnahmen. Das Ziel dieser Arbeit liegt in der Entwicklung einer Zustandsüberwachung für Gummi-Metall-Elemente. Die Herausforderungen dieser Zustandsüberwachung leiten sich aus dem viskoelastischen Verhalten sowie dem komplexen Degradationsverhalten der Elemente ab. Infolge der daraus resultierenden Unsicherheiten werden die Elemente heutzutage präventiv instandgehalten. In Lebensdauerversuchen der Gummi-Metall-Elemente werden drei Messgrößen detektiert. Dabei wird mit der Temperatur eine Messgröße identifiziert, die am geeignetsten zur Beschreibung des Zustands der Elemente ist. Generell wird die Genauigkeit einer Zustandsüberwachung durch verschiedene Unsicherheiten beeinflusst. Für die Prognose der nutzbaren Restlebensdauer der Gummi-Metall-Elemente wird das Partikelfilter, eine verbreitete modellbasierte Methode zur Zustandsüberwachung technischer Systeme, weiterentwickelt, um Unsicherheiten im Verhalten und der Degradation der Elemente zu berücksichtigen. Anhand der Ergebnisse wird belegt, dass aufbauend auf dieser Zustandsüberwachung die Ausnutzung der Gummi-Metall-Elemente in realen Anwendungen durch eine präventive Instandhaltung erhöht werden kann. Damit bildet diese Arbeit die Basis für zukünftige, prädiktive Instandhaltungskonzepte für diese Elemente. Weiterhin bestätigt die Arbeit, dass eine Berücksichtigung vorliegender Unsicherheiten zu einem frühen Zeitpunkt im Entwicklungsprozess des Zustandsüberwachungssystems empfehlenswert ist.
Keywords: Zustandsüberwachung; Prognose der Restlebensdauer; modellbasierte Prognose; Partikelfilter; Unsicherheiten; Gummi; Verlässlichkeit; Lebensdauerversuche; Predictive Maintenance

FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics - Ghasemzadeh Mohammadi, Hassan; Jentzsch, Felix; Kuschel, Maurice; Arshad, Rahil; Rautmare, Sneha; Manjunatha, Suraj; Platzner, Marco; Boschmann, Alexander; Schollbach, Dirk (Machine Learning and Principles and Practice of Knowledge Discovery in Databases)

Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization - Schemmel, Reinhard; Krieger, Viktor; Hemsel, Tobias; Sextro, Walter (Microelectronics Reliability)
Ultrasonic wire bonding is a solid-state joining process, used in the electronics industry to form electrical connections, e.g. to connect electrical terminals within semiconductor modules. Many process parameters affect the bond strength, such like the bond normal force, ultrasonic power, wire material and bonding frequency. Today, process design, development, and optimization is most likely based on the knowledge of process engineers and is mainly performed by experimental testing. In this contribution, a newly developed simulation tool is presented, to reduce time and costs and efficiently determine optimized process parameter. Based on a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire bonding process are considered using finite element simulation for the complex plastic deformation of the wire and reduced order models for the transient dynamics of the transducer, wire, substrate and bond formation. The model parameters such as the coefficients of friction between bond tool and wire and between wire and substrate were determined for aluminium and copper wire in experiments with a test rig specially developed for the requirements of heavy wire bonding. To reduce simulation time, for the finite element simulation a restart analysis and high performance computing is utilized. Detailed analysis of the bond formation showed, that the normal pressure distribution in the contact between wire and substrate has high impact on bond formation and distribution of welded areas in the contact area.
Keywords: Ultrasonic heavy wire bonding; Co-simulation; ANSYS; MATLAB; Process optimization; Friction coefficient; Copper-copper; Aluminium-copper

Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties - Bender, Amelie; Sextro, Walter (Proceedings of the European Conference of the PHM Society 2021)
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.
Keywords: Hybrid prediction method; Multi-model particle filtering; Uncertainty quantification; RUL estimation

On the applicability of time series features as health indicators for technical systems operating under varying conditions - Aimiyekagbon, Osarenren Kennedy; Bender, Amelie; Sextro, Walter (Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021))
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.
Keywords: Wind turbine diagnostics; bearing diagnostics; non-stationary operating conditions; varying operating conditions; feature extraction; feature selection; fault detection; failure detection

Rule-based Diagnostics of a Production Line - Aimiyekagbon, Osarenren Kennedy; Muth, Lars; Wohlleben, Meike Claudia; Bender, Amelie; Sextro, Walter (Proceedings of the European Conference of the PHM Society 2021)
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.
Keywords: PHME 2021; Feature Selection Classification; Feature Selection Clustering; Interpretable Model; Transparent Model; Industry 4.0; Real-World Diagnostics; Quality Control; Predictive Maintenance

Anomaly detection in hot forming processes using hybrid modeling - Lenz, Cederic; Henke, Christian; Trächtler, Ansgar (2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA))

Efficient semi-analytical simulation of elastic guided waves in cylinders subject to arbitrary non-symmetric loads - Itner, Dominik; Gravenkamp, Hauke; Dreiling, Dmitrij; Feldmann, Nadine; Henning, Bernd (Ultrasonics)

HighPerMeshes – A Domain-Specific Language for Numerical Algorithms on Unstructured Grids - Alhaddad, Samer; Förstner, Jens; Groth, Stefan; Grünewald, Daniel; Grynko, Yevgen; Hannig, Frank; Kenter, Tobias; Pfreundt, Franz-Josef; Plessl, Christian; Schotte, Merlind; Steinke, Thomas; Teich, Jürgen; et al. (Euro-Par 2020: Parallel Processing Workshops)
Solving partial differential equations on unstructured grids is a cornerstone of engineering and scientific computing. Nowadays, heterogeneous parallel platforms with CPUs, GPUs, and FPGAs enable energy-efficient and computationally demanding simulations. We developed the HighPerMeshes C++-embedded Domain-Specific Language (DSL) for bridging the abstraction gap between the mathematical and algorithmic formulation of mesh-based algorithms for PDE problems on the one hand and an increasing number of heterogeneous platforms with their different parallel programming and runtime models on the other hand. Thus, the HighPerMeshes DSL aims at higher productivity in the code development process for multiple target platforms. We introduce the concepts as well as the basic structure of the HighPerMeshes DSL, and demonstrate its usage with three examples, a Poisson and monodomain problem, respectively, solved by the continuous finite element method, and the discontinuous Galerkin method for Maxwell’s equation. The mapping of the abstract algorithmic description onto parallel hardware, including distributed memory compute clusters, is presented. Finally, the achievable performance and scalability are demonstrated for a typical example problem on a multi-core CPU cluster.
Keywords: tet_topic_hpc

Evaluating the Design Space for Offloading 3D FFT Calculations to an FPGA for High-Performance Computing - Ramaswami, Arjun; Kenter, Tobias; Kühne, Thomas; Plessl, Christian (Applied Reconfigurable Computing. Architectures, Tools, and Applications)

The HighPerMeshes framework for numerical algorithms on unstructured grids - Alhaddad, Samer; Förstner, Jens; Groth, Stefan; Grünewald, Daniel; Grynko, Yevgen; Hannig, Frank; Kenter, Tobias; Pfreundt, Franz‐Josef; Plessl, Christian; Schotte, Merlind; Steinke, Thomas; Teich, Jürgen; et al. (Concurrency and Computation: Practice and Experience)
Keywords: tet_topic_hpc

Die Pluralität von Offenbarungsmodellen in der Analytischen Theologie - Grössl, Johannes (Analytische und kontinentale Philosophie im Dialog)

Datenbewusstsein im Kontext digitaler Kompetenzen für einen selbstbestimmten Umgang mit datengetriebenen digitalen Artefakten - Höper, Lukas; Schulte, Carsten (51. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2021 - Computer Science & Sustainability, Berlin, Germany, 27. September - 1. Oktober, 2021)

Zur neuen Bedeutung von Daten in Data Science und künstlicher Intelligenz - Höper, Lukas; Podworny, Susanne; Hüsing, Sven; Schulte, Carsten; Fleischer, Yannik; Biehler, Rolf; Frischemeier, Daniel; Malatyali, Hülya (19. GI-Fachtagung Informatik und Schule, INFOS 2021, Wuppertal, Germany, September 8-10, 2021)

Integration fachwissenschaftlicher und fachdidaktischer Komponenten in der Lehramtsausbildung Mathematik Grundschule am Beispiel einer Veranstaltung zur Leitidee „Daten, Häufigkeit und Wahrscheinlichkeit“ - Frischemeier, Daniel; Podworny, Susanne; Biehler, Rolf (Konzepte und Studien zur Hochschuldidaktik und Lehrerbildung Mathematik)

Load Data Acquisition in Rural East Africa for the Layout of Microgrids and Demand–Side–Management Measures - Kakande, Josephine Nakato; Philipo, Godiana Hagile; Krauter, Stefan (Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021))
With its growing population and industrialization, DREs, and solar technologies in particular, provide a sustainable means of bridging the current energy deficit in Africa, increasing supply reliability and meeting future demand. Data acquisition and data management systems allow real time monitoring and control of energy systems as well as performance analysis. However commercial data acquisition systems often have cost implications that are prohibitive for small PV systems and installations in developing countries. In this paper, a multi-user, multi-purpose microgrid database system is designed and implemented. MAVOWATT 270 power quality analyzers by GOSSEN METRAWATT, raspberry pi modules and sensors are used for measuring, recording and storing electrical and meteorological data in East Africa. Socio-economic data is also stored in the database. The designed system employs open source software and hardware solutions which are best suited to developing regions like East Africa due to the lower cost implications. The expected results promise a comprehensive database covering different electro-technical and socio-economic parameters useful for optimal design of microgrid systems.
Keywords: Art-D; Afrika; Demand side management; MySQL; Raspberry pi; Data acquisition

Methodenentwicklung zur numerischen Lebensdauerprognose von hyperelastischen Klebverbindungen infolge zyklischer Beanspruchung mittels bruchmechanischer Ansätze - Tews, Karina; Aubel, Tobias; Meschut, Gerson; Duffe, Tobias; Kullmer, Gunter

Surface modifications reducing the adhesion of aluminum in twin roll casting applications

Digitale Transformation von Dienstleistungssystemen – Beidhändige Innovationen für vernetzte Wertschöpfungsszenarien - Beverungen, Daniel; Wolf, Verena; Bartelheimer, Christian; Franke, Alena (Dienstleistungsinnovationen durch Digitalisierung)

Low-Cost NB-IoT Microgrid Power Quality Monitoring System - Petrov, Dmitry; Kroschewski, Konstantin; Mwammenywa, Ibrahim; Kagarura, Geoffrey Mark; Hilleringmann, Ulrich (2021 IEEE Sensors)