@inproceedings{9879,
  abstract     = {{Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.}},
  author       = {{Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}},
  booktitle    = {{Prognostics and Health Management (PHM), 2014 IEEE Conference on}},
  keywords     = {{ageing, particle filtering (numerical methods), proton exchange membrane fuel cells, remaining life assessment, PEM fuel cell prognostics, PHM, RUL predictions, accelerated degradation, adaptive particle filter algorithm, dynamic loading, model parameter adaptation, prognostics and health management, proton exchange membrane fuel cells, remaining useful life estimation, self-healing effect, Adaptation models, Data models, Degradation, Estimation, Fuel cells, Mathematical model, Prognostics and health management}},
  pages        = {{1--6}},
  title        = {{{PEM fuel cell prognostics using particle filter with model parameter adaptation}}},
  doi          = {{10.1109/ICPHM.2014.7036406}},
  year         = {{2014}},
}

@inproceedings{9880,
  abstract     = {{With the paradigm shift towards prognostic and health management (PHM) of machinery, there is need for reliable PHM methodologies with narrow error bounds to allow maintenance engineers take decisive maintenance actions based on the prognostic results. Prognostics is mainly concerned with the estimation of the remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods is usually a function of the features extracted from the raw data obtained from sensors. In cases where the extracted features do not display clear degradation trends, for instance highly loaded bearings, the accuracy of the state of the art PHM methods is significantly affected. The data which lacks clear degradation trend is referred to as non-trending data. This study presents a method for extracting degradation trends from non-trending condition monitoring data for RUL estimation. The raw signals are first filtered using a discrete wavelet transform (DWT) denoising filter to remove noise from the acquired signals. Time domain, frequency domain and time-frequency domain features are then extracted from the filtered signals. An autoregressive model is then applied to the extracted features to identify the degradation trends. Features representing the maximum health information are then selected based on a performance evaluation criteria using extreme learning machine (ELM) algorithm. The selected features can then be used as inputs in a prognostic algorithm. The feasibility of the method is demonstrated using experimental bearing vibration data. The performance of the method is evaluated on the accuracy of RUL estimation and the results show that the method can be used to accurately estimate RUL with a maximum error of 10\%.}},
  author       = {{Kimotho, James Kuria and Sextro, Walter}},
  booktitle    = {{Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}},
  keywords     = {{autoregressive model ELM feature extraction feature selection non-trending Remaining useful Life}},
  title        = {{{An approach for feature extraction and selection from non-trending data for machinery prognosis}}},
  volume       = {{5}},
  year         = {{2014}},
}

@article{9881,
  abstract     = {{The increasing demand for high reliability, safety and availability of technical systems calls for innovative maintenance strategies. The use of prognostic health management (PHM) approach where maintenance action is taken based on current and future health state of a component or system is rapidly gaining popularity in the maintenance industry. Multiclass support vector machines (MC-SVM) has been identified as a promising algorithm in PHM applications due to its high classification accuracy. However, it requires parameter tuning for each application, with the objective of minimizing the classification error. This is a single objective optimization problem which requires the use of optimization algorithms that are capable of exhaustively searching for the global optimum parameters. This work proposes the use of hybrid differential evolution (DE) and particle swarm optimization (PSO) in optimally tuning the MC-SVM parameters. DE identifies the search limit of the parameters while PSO finds the global optimum within the search limit. The feasibility of the approach is verified using bearing run-to-failure data and the results show that the proposed method significantly increases health state classification accuracy.}},
  author       = {{Kimotho, James Kuria and Sextro, Walter}},
  issn         = {{1617-7061}},
  journal      = {{PAMM}},
  number       = {{1}},
  pages        = {{815--816}},
  publisher    = {{WILEY-VCH Verlag}},
  title        = {{{Optimal Parameter Tuning for Multiclass Support Vector Machines in Machinery Health State Estimation}}},
  doi          = {{10.1002/pamm.201410388}},
  volume       = {{14}},
  year         = {{2014}},
}

@article{9882,
  abstract     = {{An automotive suspension system represents one of the most complex and important systems in a passenger vehicle, which has to ensure a robust and optimized contact between the wheels and the road at any time. For improving a suspension system it is important to take an investigative look at the interaction between suspension, tire and road dynamics. Thus a part of a study into aspects of suspension modeling on multi-body simulations of rear multi-link suspension system dynamics with focus on the tire footprint area is presented in this work.}},
  author       = {{Kohl, Sergej and Sextro, Walter and Zuber, Armin}},
  issn         = {{1617-7061}},
  journal      = {{PAMM}},
  number       = {{1}},
  pages        = {{65--66}},
  publisher    = {{WILEY-VCH Verlag}},
  title        = {{{Tire footprint analysis depending on the elastokinematics of a multi-link suspension system using multi-body dynamics simulation}}},
  doi          = {{10.1002/pamm.201410020}},
  volume       = {{14}},
  year         = {{2014}},
}

@inbook{9883,
  author       = {{Meyer, Tobias and Priesterjahn, Claudia 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        = {{189--190}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Conclusion and Outlook}}},
  doi          = {{10.1007/978-3-642-53742-4_5}},
  year         = {{2014}},
}

@inproceedings{9884,
  abstract     = {{So-called reliability adaptive systems are able to adapt their system behavior based on the current reliability of the system. This allows them to react to changed operating conditions or faults within the system that change the degradation behavior. To implement such reliability adaptation, self-optimization can be used. A self-optimizing system pursues objectives, of which the priorities can be changed at runtime, in turn changing the system behavior. When including system reliability as an objective of the system, it becomes possible to change the system based on the current reliability as well. This capability can be used to control the reliability of the system throughout its operation period in order to achieve a pre-defined or user-selectable system lifetime. This way, optimal planning of maintenance intervals is possible while also using the system capabilities to their full extent. Our proposed control system makes it possible to react to changed degradation behavior by selecting objectives of the self-optimizing system and in turn changing the operating parameters in a closed loop. A two-stage controller is designed which is used to select the currently required priorities of the objectives in order to fulfill the desired usable lifetime. Investigations using a model of an automotive clutch system serve to demonstrate the feasibility of our controller. It is shown that the desired lifetime can be achieved reliably.}},
  author       = {{Meyer , Tobias and Sextro, Walter}},
  booktitle    = {{Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}},
  keywords     = {{self-optimization reliability adaptive}},
  title        = {{{Closed-loop Control System for the Reliability of Intelligent Mechatronic Systems}}},
  volume       = {{5}},
  year         = {{2014}},
}

@article{9885,
  abstract     = {{Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior.}},
  author       = {{Meyer , Tobias and Sondermann-Wölke, Christoph and Sextro, Walter}},
  journal      = {{Conference Proceedings of the 2nd International Conference on System-Integrated Intelligence}},
  keywords     = {{Self-optimization, multiobjective optimization, objective function, dependability, intelligent system, behavior adaptation}},
  pages        = {{46--53}},
  title        = {{{Method to Identify Dependability Objectives in Multiobjective Optimization Problem}}},
  doi          = {{10.1016/j.protcy.2014.09.033}},
  volume       = {{15}},
  year         = {{2014}},
}

@inproceedings{9887,
  abstract     = {{A model to calculate the locally resolved tangential contact forces of the wheel rail contact with respect to contact kinematics, material and surface properties as well as temperature is introduced. The elasticity of wheel and rail is modeled as an elastic layer consisting of point contact elements connected by springs to each other and to the wheel. Each element has two degrees of freedom in tangential directions. The resulting total stiffness matrix is reduced to calculate only the position of the elements in contact. Friction forces as well as contact stiffnesses are incorporated by a nonlinear force-displacement characteristic, which originates from a detailed contact model. The contact elements are transported through the contact zone in discrete time steps. After each time step an equilibrium is calculated. For all elements, their temperature and its influence on local friction are regarded by calculating friction power and temperature each time step.}},
  author       = {{Neuhaus, Jan and Sextro, Walter}},
  booktitle    = {{Proceedings of the 5th International Conference on Computational Methods}},
  editor       = {{Liu, G.R. and Guan, Z.W.}},
  keywords     = {{Rolling Contact, Discrete Elements, Contact Stiffness, Temperature}},
  publisher    = {{ScienTech Publisher}},
  title        = {{{Thermo-Mechanical Model for Wheel Rail Contact using Coupled Point Contact Elements}}},
  year         = {{2014}},
}

@article{9888,
  abstract     = {{This paper discusses the refinement of multibody models by integration of flexible bodies and by considering nonlinearities from contacts. It presents common approaches for contact modeling in multibody simulations and strategies to include flexible bodies. A contact model is implemented in the elastic multibody model. Experimental results show that significant effects of system dynamics can be modeled by use of a multibody model including elastic bodies and contacts.}},
  author       = {{Schulze, Sebastian and Sextro, Walter and Grüter, Frank}},
  issn         = {{1617-7061}},
  journal      = {{PAMM}},
  number       = {{1}},
  pages        = {{39--40}},
  publisher    = {{WILEY-VCH Verlag}},
  title        = {{{Contact Modeling in Multibody Systems with Elastic Bodies in High-Frequency Applications}}},
  doi          = {{10.1002/pamm.201410012}},
  volume       = {{14}},
  year         = {{2014}},
}

@inproceedings{9889,
  abstract     = {{A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test.}},
  author       = {{Sprock, Christian and Sextro, Walter}},
  booktitle    = {{Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International}},
  keywords     = {{bending, dynamic testing, measurement, structural engineering, vibrations, measurement durations, mechanical structures, multisine measurement technique, nonlinear peak bending behavior, prediction method, time-efficient dynamic analysis, Heuristic algorithms, Nonlinear systems, Oscillators, Time measurement, Time-frequency analysis, Vibrations}},
  pages        = {{320--324}},
  title        = {{{Time-efficient dynamic analysis of structures exhibiting nonlinear peak bending}}},
  doi          = {{10.1109/I2MTC.2014.6860760}},
  year         = {{2014}},
}

@inproceedings{9890,
  abstract     = {{Many nonlinear mechanical oscillators show excitation-dependent behavior. In this paper, a new measurement approach is presented to analyze such structures. The main advantage of the presented method is the high efﬁciency, since measurement duration and loads to the structure are signiﬁcantly reduced.}},
  author       = {{Sprock, Christian and Sextro, Walter}},
  booktitle    = {{Proceedings in Applied Mathematics and Mechanics 14 (2014), Nr. 1,}},
  pages        = {{293--294}},
  title        = {{{Time-efficient analysis of nonlinear dynamic behavior.}}},
  year         = {{2014}},
}

@inproceedings{9891,
  abstract     = {{A measurement method is presented that combines the advantages of the multisine measurement technique with a prediction method for peak bending behavior. This combination allows the analysis of the dynamic behavior of mechanical structures at distinctly reduced measurement durations and has the advantage of reducing high excitation impacts on the structure under test. After a brief presentation of the algorithm, the validity scope of the approach is investigated with emphasis on an exemplary error investigation.}},
  author       = {{Sprock, Christian and Sextro, Walter}},
  booktitle    = {{Proceedings of ISMA - International Conference of Noise and Vibration. 2014}},
  pages        = {{1--8}},
  title        = {{{Time-efficient analysis of nonlinear peak bending behavior.}}},
  year         = {{2014}},
}

@inproceedings{9892,
  author       = {{Sprock, Christian and Sextro, Walter}},
  booktitle    = {{Proceedings of 31st Danubia-Adria Symposium. 2014}},
  title        = {{{Vibration Analysis of Mechanical Structures using Multisine Excitation Techniques}}},
  year         = {{2014}},
}

@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}},
}

@article{15966,
  author       = {{Niendorf, Thomas and Leuders, Stefan and Riemer, Andre and Brenne, Florian and Tröster, Thomas and Richard, Hans Albert and Schwarze, Dieter}},
  issn         = {{1438-1656}},
  journal      = {{Advanced Engineering Materials}},
  pages        = {{857--861}},
  title        = {{{Functionally Graded Alloys Obtained by Additive Manufacturing}}},
  doi          = {{10.1002/adem.201300579}},
  year         = {{2014}},
}

@article{15967,
  author       = {{Riemer, A. and Leuders, S. and Thöne, M. and Richard, H.A. and Tröster, Thomas and Niendorf, T.}},
  issn         = {{0013-7944}},
  journal      = {{Engineering Fracture Mechanics}},
  pages        = {{15--25}},
  title        = {{{On the fatigue crack growth behavior in 316L stainless steel manufactured by selective laser melting}}},
  doi          = {{10.1016/j.engfracmech.2014.03.008}},
  year         = {{2014}},
}

@inproceedings{16097,
  author       = {{Klein, M. and Hülsbusch, D. and Walther, F. and Bartsch, M. and Hausmann, J. and Frantz, M. and Lauter, C. and Tröster, Thomas}},
  isbn         = {{978-3-88355-402-0}},
  location     = {{Stade}},
  pages        = {{188--193}},
  title        = {{{Characterization of the corrosion influence on the fatigue behavior of intrinsic CFRP-steel-hybrids}}},
  year         = {{2014}},
}

