@inproceedings{SchmidHermannBhogarajuetal.2022, author = {Schmid, Maximilian and Hermann, Joseph and Bhogaraju, Sri Krishna and Elger, Gordon}, title = {Reliability of SAC Solders under Low and High Stress Conditions}, booktitle = {2022 IEEE 9th Electronics System-Integration Technology Conference (ESTC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-8947-8}, doi = {https://doi.org/10.1109/ESTC55720.2022.9939394}, pages = {553 -- 559}, year = {2022}, language = {en} } @article{TerbrackStoettnerEndisch2022, author = {Terbrack, Christoph and St{\"o}ttner, Julia and Endisch, Christian}, title = {Design and Validation of the Parallel Enhanced Commutation Integrated Nested Multilevel Inverter Topology}, volume = {37}, journal = {IEEE Transactions on Power Electronics}, number = {12}, publisher = {IEEE}, address = {Piscataway}, issn = {1941-0107}, doi = {https://doi.org/10.1109/TPEL.2022.3183859}, pages = {15163 -- 15174}, year = {2022}, language = {en} } @article{TheisslerPerezVelazquezKettelgerdesetal.2021, author = {Theissler, Andreas and P{\´e}rez-Vel{\´a}zquez, Judith and Kettelgerdes, Marcel and Elger, Gordon}, title = {Predictive maintenance enabled by machine learning}, volume = {2021}, pages = {107864}, journal = {Reliability Engineering \& System Safety}, subtitle = {use cases and challenges in the automotive industry}, number = {215}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0951-8320}, doi = {https://doi.org/10.1016/j.ress.2021.107864}, year = {2021}, abstract = {Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data.}, language = {en} } @article{StoettnerRauscherEndisch2022, author = {St{\"o}ttner, Julia and Rauscher, Andreas and Endisch, Christian}, title = {Pareto optimization of multilevel inverter structures regarding the DC magnitude, switching frequency and switching angles}, volume = {2022}, pages = {108259}, journal = {International Journal of Electrical Power \& Energy Systems}, number = {142, Part A}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0142-0615}, doi = {https://doi.org/10.1016/j.ijepes.2022.108259}, year = {2022}, language = {en} } @article{SchmidVoegeleEndisch2020, author = {Schmid, Michael and V{\"o}gele, Ulrich and Endisch, Christian}, title = {A novel matrix-vector-based framework for modeling and simulation of electric vehicle battery packs}, volume = {2020}, pages = {101736}, journal = {Journal of Energy Storage}, number = {32}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-152X}, doi = {https://doi.org/10.1016/j.est.2020.101736}, year = {2020}, language = {en} } @article{RauscherKaiserDevarajuetal.2024, author = {Rauscher, Andreas and Kaiser, Johannes and Devaraju, Manoj and Endisch, Christian}, title = {Deep learning and data augmentation for partial discharge detection in electrical machines}, volume = {133}, pages = {108074}, journal = {Engineering Applications of Artificial Intelligence}, number = {Part A}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1873-6769}, doi = {https://doi.org/10.1016/j.engappai.2024.108074}, year = {2024}, abstract = {Fault testing in the production line of automotive traction machines is essential to ensure the desired lifetime. Since repetitive partial discharges (PDs) caused by anomalies in the insulation system lead to premature breakdowns of electrical machines, a reliable PD detection is of great importance. This paper proposes deep learning (DL) methods to improve the discrimination of PD from background noise in comparison with the state-of-the-art amplitude based PD detection in the production line. First, a systematic data extraction and labeling procedure is introduced to obtain correctly labeled datasets from arbitrary PD measurements. In addition, datasets are enhanced with low signal-to-noise ratio PD pulses by applying a special data augmentation approach. 13 different convolutional, recurrent and fully connected neural networks are compared for various time-frequency representations of the input signals. Hyperparameters for input transform, network topology and solver are optimized for all 13 combinations to ensure a fair case study. As a result, the two-dimensional convolutional neural network with continuous wavelet transform achieves the best accuracy of around 99.76\% on a test dataset of PD signals originating from previously not utilized test objects. All DL models considered in this comparison outperform the state-of-the-art threshold-based PD classification. Even for PD events with an amplitude close to the noise level, the detection rate is still around 95\% for the best network. Furthermore, without applying the proposed data augmentation procedure, the DL models investigated are not able to distinguish small PD pulses from noise.}, language = {en} } @article{LiuContiBhogarajuetal.2020, author = {Liu, E and Conti, Fosca and Bhogaraju, Sri Krishna and Signorini, Raffaella and Pedron, Danilo and Wunderle, Bernhard and Elger, Gordon}, title = {Thermomechanical stress in GaN-LEDs soldered onto Cu substrates studied using finite element method and Raman spectroscopy}, volume = {51}, journal = {Journal of Raman Spectroscopy}, number = {10}, publisher = {Wiley}, address = {Chichester}, issn = {1097-4555}, doi = {https://doi.org/10.1002/jrs.5947}, pages = {2083 -- 2094}, year = {2020}, abstract = {Local thermomechanical stress can cause failures in semiconductor packages during long-time operation under harsh environmental conditions. This study helps to explain the packaging-induced stress in blue GaN-LEDs soldered onto copper substrates using AuSn alloy as lead-free interconnect material. Based on the finite element method, a virtual prototype is developed to simulate the thermomechanical behavior and stress in the LED and in the complete LED/AuSn/Cu assembly considering plastic and viscoplastic strain. The investigations were performed by varying the temperature between -50°C and 180°C. To validate the model, the simulation results are compared to experimental data collected with Raman spectroscopy. Studies of the urn:x-wiley:03770486:media:jrs5947:jrs5947-math-0003 phonon mode of GaN semiconductor are elaborated to understand the induced thermomechanical stress. The model enables evaluation of the stress in the interfaces of the assembly, which otherwise cannot be accessed by measurements. It serves to predict how assemblies would perform, before committing resources to build a physical prototype.}, language = {en} } @article{MayerClassenEndisch2021, author = {Mayer, Sebastian and Classen, Tobias and Endisch, Christian}, title = {Modular production control using deep reinforcement learning: proximal policy optimization}, volume = {32}, journal = {Journal of Intelligent Manufacturing}, number = {8}, publisher = {Springer Nature}, address = {Cham}, issn = {1572-8145}, doi = {https://doi.org/10.1007/s10845-021-01778-z}, pages = {2335 -- 2351}, year = {2021}, abstract = {EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent's action, state, and reward design.}, language = {en} } @article{CazaresGarciaContrerasPerezVelazquez2020, author = {Cazares, Adri{\´a}n and Garc{\´i}a-Contreras, Rodolfo and P{\´e}rez-Vel{\´a}zquez, Judith}, title = {Eco-evolutionary effects of bacterial cooperation on phage therapy}, volume = {11}, pages = {590294}, journal = {Frontiers in Microbiology}, subtitle = {an unknown risk?}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-302X}, doi = {https://doi.org/10.3389/fmicb.2020.590294}, year = {2020}, abstract = {If there is something we have learned from the antibiotic era, it is that indiscriminate use of a therapeutic agent without a clear understanding of its long-term evolutionary impact can have enormous health repercussions. This knowledge is particularly relevant when the therapeutic agents are remarkably adaptable and diverse biological entities capable of a plethora of interactions, most of which remain largely unexplored. Although phage therapy (PT) undoubtedly holds the potential to save lives, its current efficacy in case studies recalls the golden era of antibiotics, when these compounds were highly effective and the possibility of them becoming ineffective seemed remote. Safe PT schemes depend on our understanding of how phages interact with, and evolve in, highly complex environments. Here, we summarize and review emerging evidence in a commonly overlooked theme in PT: bacteria-phage interactions. In particular, we discuss the influence of quorum sensing (QS) on phage susceptibility, the consequent role of phages in modulating bacterial cooperation, and the potential implications of this relationship in PT, including how we can use this knowledge to inform PT strategies. We highlight that the influence of QS on phage susceptibility seems to be widespread but can have contrasting outcomes depending on the bacterial host, underscoring the need to thoroughly characterize this link in various bacterial models. Furthermore, we encourage researchers to exploit competition experiments, experimental evolution, and mathematical modeling to explore this relationship further in relevant infection models. Finally, we emphasize that long-term PT success requires research on phage ecology and evolution to inform the design of optimal therapeutic schemes.}, language = {en} } @article{KleinerKomsiyskaElgeretal.2019, author = {Kleiner, Jan and Komsiyska, Lidiya and Elger, Gordon and Endisch, Christian}, title = {Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment}, volume = {13}, pages = {62}, journal = {Energies}, subtitle = {influences of the experimental validation setup}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {https://doi.org/10.3390/en13010062}, year = {2019}, abstract = {In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro-thermal model is developed and experimentally validated to predict the cell's temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell's experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell's thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile.}, language = {en} }