@inbook{9893, author = {{Trächtler, Ansgar and Hölscher, Christian and Rasche, Christoph and Priesterjahn, Claudia and Zimmer, Detmar and Henning Keßler, Jan and Stahl, Katharin and Flaßkamp, Kathrin and Vaßholz, Mareen and Krüger, Martin and Dellnitz, Michael and Iwanek, Peter and Reinold, Peter and Hartmann, Philip and Meyer, Tobias and Sextro, Walter}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, editor = {{Gausemeier, Jürgen and Josef Rammig, Franz and Schäfer, Wilhelm and Sextro, Walter}}, isbn = {{978-3-642-53741-7}}, pages = {{1--24}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Introduction to Self-optimization and Dependability}}}, doi = {{10.1007/978-3-642-53742-4_1}}, year = {{2014}}, } @inbook{9894, author = {{Trächtler, Ansgar and Kleinjohann, Bernd and Heinzemann, Christian and Rasche, Christoph and Priesterjahn, Claudia and Steenken, Dominik and Wehrheim, Heike and Gausemeier, Jürgen and Flaßkamp, Kathrin and Kleinjohann, Lisa and Krüger, Martin and Iwanek, Peter and Hartmann, Philip and Dorociak, Rafal and Groesbrink, Stefan and Ziegert, Steffen and Meyer, Tobias and Sextro, Walter and Schäfer, Wilhelm}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, editor = {{Gausemeier, Jürgen and Josef Rammig, Franz and Schäfer, Wilhelm and Sextro, Walter}}, isbn = {{978-3-642-53741-7}}, pages = {{173--188}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Case Study}}}, doi = {{10.1007/978-3-642-53742-4_4}}, year = {{2014}}, } @inproceedings{9895, abstract = {{Power semiconductor modules are used to control and switch high electrical currents and voltages. Within the power module package wire bonding is used as an interconnection technology. In recent years, aluminum wire has been used preferably, but an ever-growing market of powerful and efficient power modules requires a material with better mechanical and electrical properties. For this reason, a technology change from aluminum to copper is indispensable. However, the copper wire bonding process reacts more sensitive to parameter changes. This makes manufacturing reliable copper bond connections a challenging task. The aim of the BMBF funded project Itsowl-InCuB is the development of self-optimizing techniques to enable the reliable production of copper bond connections under varying conditions. A model of the process is essential to achieve this aim. This model needs to include the dynamic elasto-plastic deformation, the ultrasonic softening effect and the proceeding adhesion between wire and substrate. This paper focusses on the pre-deformation process. In the touchdown phase, the wire is pressed into the V-groove of the tool and a small initial contact area between wire and substrate arise. The local characteristics of the material change abruptly because of the cold forming. Consequently, the pre-deformation has a strong effect on the joining process. In [1], a pre-cleaning effect during the touchdown process of aluminum wires by cracking of oxide layers was presented. These interactions of the process parameters are still largely unknown for copper. In a first step, this paper validates the importance of modeling the pre-deformation by showing its impact on the wire deformation characteristic experimentally. Creating cross-section views of pre-deformed copper wires has shown a low deformation degree compared to aluminum. By using a digital microscope and a scanning confocal microscope an analysis about the contact areas and penetration depths after touchdown has been made. Additionally, it has to be taken into account that the dynamical touchdown force depends on the touchdown speed and the touchdown force set in the bonding machine. In order to measure the overshoot in the force signals, a strain gauge sensor has been used. Subsequently, the affecting factors have been interpreted independently Furthermore, the material properties of copper wire have been investigated with tensile tests and hardness measurements. In a second step, the paper presents finite element models of the touchdown process for source and destination bonds. These models take the measured overshoot in the touchdown forces into account. A multi-linear, isotropic material model has been selected to map the material properties of the copper. A validation of the model with the experimental determined contact areas, normal pressures and penetration depths reveals the high model quality. Thus, the simulation is able to calculate and visualize the three dimensional pre-deformation with an integrated material parameter of the wire if the touchdown parameters of the bonding machine are known. Based on the calculated deformation degrees of wire and substrate, it is probably possible to investigate the effect of the pre-deformation on the pre-cleaning phase in the copper wire bonding.}}, author = {{Unger, Andreas and Sextro, Walter and Althoff, Simon and Eichwald, Paul and Meyer, Tobias and Eacock, Florian and Brökelmann, Michael}}, booktitle = {{Proceedings of the 47th International Symposium on Microelectronics (IMAPS)}}, keywords = {{pre-deformation, copper wire bonding, finite element model}}, pages = {{289--294}}, title = {{{Experimental and Numerical Simulation Study of Pre-Deformed Heavy Copper Wire Wedge Bonds}}}, year = {{2014}}, } @inproceedings{9896, abstract = {{In power electronics, ultrasonic wire bonding is used to connect the electrical terminals of power modules. To implement a self-optimization technique for ultrasonic wire bonding machines, a model of the process is essential. This model needs to include the so called ultrasonic softening effect. It is a key effect within the wire bonding process primarily enabling the robust interconnection between the wire and a substrate. However, the physical modeling of the ultrasonic softening effect is notoriously difficult because of its highly non-linear character and the absence of a proper measurement method. In a first step, this paper validates the importance of modeling the ultrasonic softening by showing its impact on the wire deformation characteristic experimentally. In a second step, the paper presents a data-driven model of the ultrasonic softening effect which is constructed from data using machine learning techniques. A typical caveat of data-driven modeling is the need for training data that cover the considered domain of process parameters in order to achieve accurate generalization of the trained model to new process configurations. In practice, however, the space of process parameters can only be sampled sparsely. In this paper, a novel technique is applied which enables the integration of prior knowledge about the process into the datadriven modeling process. It turns out that this approach results in accurate generalization of the data-driven model to unseen process parameters from sparse data.}}, author = {{Unger, Andreas and Sextro, Walter and Althoff, Simon and Meyer, Tobias and Brökelmann, Michael and Neumann, Klaus and Reimann, René Felix and Guth, Karsten and Bolowski, Daniel}}, booktitle = {{Proceedings of 8th International Conference on Integrated Power Electronic Systems}}, pages = {{175--180}}, title = {{{Data-driven Modeling of the Ultrasonic Softening Effect for Robust Copper Wire Bonding}}}, volume = {{141}}, year = {{2014}}, } @inbook{20085, author = {{Trächtler, Ansgar and Kleinjohann, Bernd and Heinzemann, Christian and Rasche, Christoph and Priesterjahn, Claudia and Steenken, Dominik and Wehrheim, Heike and Gausemeier, Jürgen and Flaßkamp, Kathrin and Kleinjohann, Lisa and Krüger, Martin and Iwanek, Peter and Hartmann, Philip and Dorociak, Rafal and Groesbrink, Stefan and Ziegert, Steffen and Meyer, Tobias and Sextro, Walter and Schäfer, Wilhelm}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, editor = {{Gausemeier, Jürgen and Josef Rammig, Franz and Schäfer, Wilhelm and Sextro, Walter}}, isbn = {{978-3-642-53741-7}}, pages = {{173--188}}, publisher = {{Springer Berlin Heidelberg}}, title = {{{Case Study}}}, doi = {{10.1007/978-3-642-53742-4_4}}, year = {{2014}}, } @inproceedings{26973, abstract = {{Self-optimizing mechatronic systems allow the adaptation of the system’s behavior to the current situation. This can be used to actively adapt the behavior to the current degradation state of the system or of some of its components. To this end, the Multi-Level Dependability Concept has been developed. In this contribution, we show how the Multi-Level Dependability Concept has been applied to the active suspension module of an innovative rail-bound vehicle. For this module, the usage of control reconfiguration, which is a novel approach to exploit complex redundancy systems, is required. We show that by combining self-optimization with the possibilities given by control reconfiguration, the dependability of a complex mechatronic system can be greatly improved.}}, author = {{Meyer, Tobias and Kessler, Jan Henning and Sextro, Walter and Trächtler, Ansgar}}, booktitle = {{The Annual Reliability and Maintainability Symposium (RAMS)}}, title = {{{Increasing Intelligent Systems’ Reliability by using Reconfiguration}}}, year = {{2013}}, } @inproceedings{22378, author = {{Meyer, Tobias and Hölscher, Christian and Menke, Michael and Sextro, Walter and Zimmer, Detmar}}, booktitle = {{PAMM - Proceedings in Applied Mathematics and Mechanics }}, number = {{1}}, pages = {{483--484}}, publisher = {{Gesellschaft für Angewandte Mathematik und Mechanik (GAMM)}}, title = {{{Multiobjective Optimization including Safety of Operation Applied to a Linear Drive System}}}, doi = {{10.1002/pamm.201310234}}, volume = {{13}}, year = {{2013}}, } @inbook{23139, author = {{Kessler, Jan Henning and Meyer, Tobias and Sextro, Walter and Sondermann-Wölke, Christoph and Trächtler, Ansgar}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, pages = {{55--62}}, publisher = {{Springer-Verlag, Heidelberg, Germany}}, title = {{{Increasing the Dependability of Self-Optimizing Systems During Operation Using the Multi-Level Dependability Concept}}}, year = {{2013}}, } @inproceedings{23140, author = {{Meyer, Tobias and Kessler, Jan Henning and Sextro, Walter and Trächtler, Ansgar}}, booktitle = {{The Annual Reliability and Maintainability Symposium (RAMS)}}, title = {{{Increasing Intelligent Systems’ Reliability by using Reconfiguration}}}, year = {{2013}}, } @inbook{23149, author = {{Hölscher, Christian and Kessler, Jan Henning and Meyer, Tobias and Rasche, Christoph and Reinold, Peter and Sextro, Walter and Sondermann-Wölke, Christoph and Zimmer, Detmar}}, booktitle = {{Dependability of Self-Optimizing Mechatronic Systems}}, pages = {{16--22}}, publisher = {{Springer-Verlag, Heidelberg, Germany}}, title = {{{Applications of Self-Optimizing Systems}}}, year = {{2013}}, } @article{9807, abstract = {{Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy /incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution -- particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MCSVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 \% error margin. The method also outperforms other soft computing methods proposed in literature.}}, author = {{Kimotho, James Kuria and Sondermann-Wölke, Christopher and Meyer, Tobias and Sextro, Walter}}, journal = {{Chemical Engineering Transactions}}, pages = {{619--624}}, title = {{{Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization}}}, doi = {{10.3303/CET1333104}}, volume = {{33}}, year = {{2013}}, } @article{9808, abstract = {{This study presents the methods employed by a team from the department of Mechatronics and Dynamics at the University of Paderborn, Germany for the 2013 PHM data challenge. The focus of the challenge was on maintenance action recommendation for an industrial machinery based on remote monitoring and diagnosis. Since an ensemble of data driven methods has been considered as the state of the art approach in diagnosis and prognosis, the first approach was to evaluate the performance of an ensemble of data driven methods using the parametric data as input and problems (recommended maintenance action) as the output. Due to close correlation of parametric data of different problems, this approach produced high misclassification rate. Event-based decision trees were then constructed to identify problems associated with particular events. To distinguish between problems associated with events that appeared in multiple problems, support vector machine (SVM) with parameters optimally tuned using particle swarm optimization (PSO) was employed. Parametric data was used as the input to the SVM algorithm and majority voting was employed to determine the final decision for cases with multiple events. A total of 165 SVM models were constructed. This approach improved the overall score from 21 to 48. The method was further enhanced by employing an ensemble of three data driven methods, that is, SVM, random forests (RF) and bagged trees (BT), to build the event based models. With this approach, a score of 51 was obtained . The results demonstrate that the proposed event based method can be effective in maintenance action recommendation based on events codes and parametric data acquired remotely from an industrial equipment.}}, author = {{Kimotho, James Kuria and Sondermann-Wölke, Chritoph and Meyer, Tobias and Sextro, Walter}}, journal = {{International Journal of Prognostics and Health Management}}, keywords = {{maintenance decision, Bagged trees, Decision trees, PSO-SVM, Random forests}}, number = {{2}}, title = {{{Application of Event Based Decision Tree and Ensemble of Data Driven Methods for Maintenance Action Recommendation}}}, volume = {{4}}, year = {{2013}}, } @inproceedings{9858, abstract = {{In this contribution, we introduce a multiobjective optimization used to calculate safe optimal working points for a mechatronic system by including stochastic safety-critical signals in an objective function. Our application example consists of a linear drive for a rail-bound vehicle and an actuation unit. The linear drive's secondary part is fixed; the primary part is vehicle-mounted and can be adjusted vertically to account for deviations of the height of the secondary part. A small air gap between both parts improves efficiency, but increases the risk of a collision between the two parts. Using height data of the secondary part, a trajectory for the vertical adjustment of the primary part is calculated. However, unexpected deviations necessitate a readjustment of the air gap. The probability of such unexpected height deviations can be calculated from the readjustment data. The system is equipped with sensors to measure the air gap. Assuming that the sensor noise is normally distributed, noise characteristics are determined. Using this information and the probability distribution of unexpected height deviations, the probability of a collision is determined.T he sensor noise and the probability of a collision between both parts of the linear drive are included in the dynamical model of the system. Using multiobjective optimization, pareto-optimal working points for the controller of the air gap are obtained. By selecting an appropriate working point, safe operation can be ensured.}}, author = {{Meyer, Tobias and Hölscher, Christina and Menke, Michael and Sextro, Walter and Zimmer, Detmar}}, booktitle = {{Proc. Appl. Math. Mech.}}, pages = {{483--484}}, title = {{{Multiobjective Optimization including Safety of Operation Applied to a Linear Drive System}}}, doi = {{10.1002/pamm.201310234}}, volume = {{13}}, year = {{2013}}, } @inproceedings{9859, abstract = {{Self-optimizing mechatronic systems allow the adaptation of the system's behavior to the current situation. This can be used to actively adapt the behavior to the current degradation state of the system or of some of its components. To this end, the Multi-Level Dependability Concept has been developed. In this contribution, we show how the Multi-Level Dependability Concept has been applied to the active suspension module of an innovative rail-bound vehicle. For this module, the usage of control reconfiguration, which is a novel approach to exploit complex redundancy systems, is required. We show that by combining self-optimization with the possibilities given by control reconfiguration, the dependability of a complex mechatronic system can be greatly improved.}}, author = {{Meyer , Tobias and Henning Keßler, Jan and Sextro, Walter and Trächtler, Ansgar}}, booktitle = {{Proceedings of the Annual Reliability and Maintainability Symposium (RAMS)}}, title = {{{Increasing Intelligent Systems' Reliability by Using Reconfiguration}}}, doi = {{10.1109/RAMS.2013.6517636}}, year = {{2013}}, } @article{9860, abstract = {{Self-optimizing mechatronic systems offer possibilities well beyond those of traditional mechatronic systems. Among these is the adaptation of the system behavior to the current situation. To do so, they are able to choose from different working points, which are pre-calculated using multiobjective optimization and are thus Pareto-optimal with regard to the chosen objective functions. In this contribution, a method is presented that allows to continuously control the system degradation by adapting the behavior of a selfoptimizing system throughout its complete lifetime. The current remaining useful lifetime is estimated and then related to the spent lifetime and the desired useful lifetime. Using this information, a reliability-related objective is prioritized using a closed-loop control, which in turn is used to determine the working point of the self-optimizing system. This way, the desired useful lifetime can be achieved. To exemplify the setup of the controller structure and to demonstrate the adaptation of the system behavior, a dynamic model of a clutch system is used. It can be seen that the closed loop controller is able to correct for external perturbations, such as changed requirements, as well as changed system parameters. This way, the modeled system is able to achieve the desired lifetime reliably.}}, author = {{Meyer, Tobias and Sondermann-Wölke, Christoph and Kimotho, James Kuria and Sextro, Walter}}, journal = {{Chemical Engineering Transactions}}, pages = {{625--630}}, title = {{{Controlling the Remaining Useful Lifetime using Self-Optimization}}}, doi = {{10.3303/CET1333105}}, volume = {{33}}, year = {{2013}}, } @inproceedings{9861, abstract = {{Selbstoptimierende mechatronische Systeme bieten die Möglichkeit, ihr Verhalten an geänderte Umgebungsbedingungen anzupassen. Dazu werden beispielsweise redundante Strukturen genutzt, Reglerparameter angepasst oder Regelstrategien umgeschaltet. Dies kann auch genutzt werden, um die Zuverlässigkeit des Systems zu steigern. Zugleich entstehen aber durch die gesteigerte Komplexität dieser Systeme zusätzliche Risiken. Um sicherzustellen, dass das System dennoch die gestellten Anforderungen bezüglich der Zuverlässigkeit erfüllt, ist eine Modellierung des Gesamtsystems und anschließende Zuverlässigkeitsbewertung notwendig. Dies ist aufgrund der situationsabhängigen Verhaltensanpassung und des nicht intuitiv vorhersehbaren Verhaltens jedoch nicht mit klassischen Verfahren möglich. Ein Modellierungsverfahren, das diese Eigenschaften abbilden kann, ist LARES (LAnguage for REconfigurable dependable Systems). Die Anwendung von LARES zur Bewertung der Zuverlässigkeit eines selbstoptimierenden Systems wird anhand des Feder-Neige-Moduls gezeigt. Es ist eine Baugruppe der Fahrzeuge eines innovativen Bahnsystems, der RailCabs. Das Feder-Neige-Modul dient dazu, unerwünschte Schwingungen des Fahrzeugaufbaus zu minimieren. Mit LARES können die Hardware-Komponenten des Systems, ihre in Abhängigkeit von der aktuellen Situation veränderten Belastungen sowie die nicht-deterministische Verhaltensadaption modelliert werden.}}, author = {{Meyer, Tobias and Sondermann-Wölke, Christoph and Sextro, Walter and Riedl, Martin and Gouberman, Alexander and Siegle, Markus}}, booktitle = {{9. Paderborner Workshop Entwurf mechatronischer Systeme}}, editor = {{Gausemeier, Jürgen and Dumitrescu, Roman and Rammig, Franz and Schäfer, Wilhelm and Trächtler, Ansgar}}, pages = {{161--174}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Bewertung der Zuverlässigkeit selbstoptimierender Systeme mit dem LARES-Framework}}}, year = {{2013}}, } @inproceedings{9791, abstract = {{The rapid development of communication and information technology opens up fascinating perspectives, which go far beyond the state of the art in mechatronics: mechatronic systems with inherent partial intelligence. These so called self-optimizing systems adapt their objectives and behavior autonomously and flexibly to changing operating conditions. On the one hand, securing the dependability of such systems is challenging due to their complexity and non-deterministic behavior. On the other hand, self-optimization can be used to increase the dependability of the system during its operation. However, it has to be ensured, that the self-optimization works dependable itself. To cope with these challenges, the multi-level dependability concept was developed. It enables predictive condition monitoring, influences the objectives of the system and determines suitable means to improve the system's dependability during its operation. In this contribution we introduce a procedure for the conceptual design of an advanced condition monitoring based on the system's principle solution. The principle solution describes the principal operation mode of the system and its desired behavior. It is modeled using the specification technique for the domain-spanning description of the principle solution of a self-optimizing system and consists of a coherent system of eight partial models (e.g. requirements, active structure, system of objectives, behavior, etc.). The partial models are analyzed separately in order to derive the components of the multi-level dependability concept. In particular, the reliability analysis of the partial model active structure is performed to identify the system elements to be monitored and parameters to be measured. The principle solution is extended accordingly: e.g. with system elements required for the realization of the dependability concept. The advantages of the method are shown on the self-optimizing guidance module of a railroad vehicle.}}, author = {{Sondermann-Wölke , Christoph and Meyer, Tobias and Dorociak, Rafal and Gausemeier, Jürgen and Sextro, Walter}}, booktitle = {{Proceedings of the 11th International Probabilistic Safety Assessment and Management Conference (PSAM11) and The Annual European Safety and Reliability Conference (ESREL2012)}}, keywords = {{Mechatronic Systems, Principle Solution, Condition Monitoring, Conceptual Design}}, title = {{{Conceptual Design of Advanced Condition Monitoring for a Self-Optimizing System based on its Principle Solution}}}, year = {{2012}}, } @inproceedings{16453, author = {{Degener, Bastian and Kempkes, Barbara and Langner, Tobias and Meyer auf der Heide, Friedhelm and Pietrzyk, Peter and Wattenhofer, Roger}}, booktitle = {{Proceedings of the 23rd ACM symposium on Parallelism in algorithms and architectures - SPAA '11}}, isbn = {{9781450307437}}, title = {{{A tight runtime bound for synchronous gathering of autonomous robots with limited visibility}}}, doi = {{10.1145/1989493.1989515}}, year = {{2011}}, } @inproceedings{30478, author = {{Meyer, Michael and Grote, Tobias and Böcker, Joachim}}, booktitle = {{2007 European Conference on Power Electronics and Applications}}, publisher = {{IEEE}}, title = {{{Direct torque control for interior permanent magnet synchronous motors with respect to optimal efficiency}}}, doi = {{10.1109/epe.2007.4417370}}, year = {{2008}}, } @inproceedings{2414, author = {{Birkenheuer, Georg and Hagelweide, Wilke and Hagemeier, Björn and Japs, Viktor and Keller, Matthias and Mayr, Nikolas and Meyer, Jan and Schumacher, Tobias and Voß, Kerstin and Zajac, Markus}}, booktitle = {{Proc. GI Informatiktage}}, pages = {{91--94}}, publisher = {{Gesellschaft für Informatik (GI)}}, title = {{{PIRANHA – Hunter of Idle Resources}}}, volume = {{2}}, year = {{2005}}, }