[{"status":"public","abstract":[{"text":"To predict and prevent uneven tire wear in addition to a reduction of overall tire wear, it is essential to estimate not only the total amount of wear but also how the wear is distributed across the tire width. This requires knowledge of the frictional power distribution in the tire contact patch, which is the basis for calculating tire wear using a wear law. Usually, only 3D structural tire models can generate such distributed contact results. However, they involve high computational costs and cannot be used for comprehensive optimization of a vehicle’s suspension system with respect to tire wear characteristics. Hence, this contribution presents a methodology on how to accelerate the prediction of the frictional power distribution using two components: The structural tire model is replaced by an empirical tire model that on its own is not able to generate distributed contact results. Therefore, an artificial neural network is trained to predict the desired contact results from the kinematic quantities calculated by the empirical tire model. In the initial training phase, both components are fitted to data generated by the original complex tire model. After training, the empirical tire model can replace the structural tire model in vehicle simulations, resulting in significantly shorter calculation times. The simulation results are fed into the artificial neural network, which predicts the frictional power distributions over the tire width with negligible additional effort. Overall, the methodology reduces calculation time for the prediction of tire wear based on virtual test drives to approximately 25% of the time needed when using structural tire models.","lang":"eng"}],"type":"journal_article","publication":"Tire Science and Technology","language":[{"iso":"eng"}],"extern":"1","article_type":"original","user_id":"77313","department":[{"_id":"151"},{"_id":"9"}],"_id":"58556","citation":{"ama":"Muth L, Zharia R, Sahin H, Sextro W. Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network. <i>Tire Science and Technology</i>. Published online 2025. doi:<a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>","chicago":"Muth, Lars, Raphael Zharia, Hürkan Sahin, and Walter Sextro. “Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network.” <i>Tire Science and Technology</i>, 2025. <a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>.","ieee":"L. Muth, R. Zharia, H. Sahin, and W. Sextro, “Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network,” <i>Tire Science and Technology</i>, 2025, doi: <a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>.","apa":"Muth, L., Zharia, R., Sahin, H., &#38; Sextro, W. (2025). Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network. <i>Tire Science and Technology</i>. <a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>","mla":"Muth, Lars, et al. “Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network.” <i>Tire Science and Technology</i>, The Tire Society, 2025, doi:<a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>.","bibtex":"@article{Muth_Zharia_Sahin_Sextro_2025, title={Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network}, DOI={<a href=\"https://doi.org/10.2346/TST-24-009\">https://doi.org/10.2346/TST-24-009</a>}, journal={Tire Science and Technology}, publisher={The Tire Society}, author={Muth, Lars and Zharia, Raphael and Sahin, Hürkan and Sextro, Walter}, year={2025} }","short":"L. Muth, R. Zharia, H. Sahin, W. Sextro, Tire Science and Technology (2025)."},"year":"2025","publication_status":"epub_ahead","quality_controlled":"1","doi":"https://doi.org/10.2346/TST-24-009","title":"Prediction of the Frictional Power Distribution in the Tire Contact Patch Based on an Empirical Tire Model and an Artificial Neural Network","author":[{"first_name":"Lars","orcid":"0000-0002-2938-5616","last_name":"Muth","id":"77313","full_name":"Muth, Lars"},{"last_name":"Zharia","full_name":"Zharia, Raphael","first_name":"Raphael"},{"first_name":"Hürkan","full_name":"Sahin, Hürkan","last_name":"Sahin"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"date_created":"2025-02-10T19:54:28Z","date_updated":"2025-02-27T19:53:09Z","publisher":"The Tire Society"},{"_id":"56113","user_id":"77313","language":[{"iso":"eng"}],"type":"journal_article","publication":"PAMM","abstract":[{"text":"Abstract This study focuses on hybrid modeling approaches that combine physical and data-driven methods to create more effective dynamical system models. In particular, it examines discrepancy models, a type of hybrid model that integrates a physical system model with data-driven compensation for inaccuracies. The study applies two discrepancy modeling methods to a multibody system using discrepancies in the state vector and its time derivative, respectively. As an application example, a four-bar linkage with nonlinear damping is investigated, using a simplified conservative system as a physical model. The comparative analysis of the two methods shows that the continuous approach generally outperforms the discrete method in terms of accuracy and computational efficiency, especially for velocity prediction and prediction horizon. However, scenarios, where input signals for training and testing differ, present nuanced findings. When the continuous method is trained on complex signals (sine) and tested on simpler ones (stair), it struggles to deliver satisfactory results, exhibiting notably higher root mean square error (RMSE) values, particularly in angular velocity prediction. Conversely, training on simple signals (stair) and testing on complex ones (sine) surprisingly yields low RMSE values, indicating the continuous method’s adaptability. While the discrete method aligns more closely with expectations and performs better in certain scenarios, its results are consistently moderate, neither exceptional nor particularly poor. The study also introduces a selection framework for choosing the most suitable algorithm based on the specific characteristics of the modeling task. This framework provides guidance for researchers and practitioners in leveraging hybrid modeling effectively. Finally, the study concludes with an outlook on future research directions.","lang":"eng"}],"status":"public","date_updated":"2025-02-27T19:54:08Z","author":[{"first_name":"Meike Claudia","last_name":"Wohlleben","orcid":"0009-0009-9767-7168","full_name":"Wohlleben, Meike Claudia","id":"43991"},{"full_name":"Röder, Benedict","last_name":"Röder","first_name":"Benedict"},{"first_name":"Henrik","last_name":"Ebel","full_name":"Ebel, Henrik"},{"first_name":"Lars","id":"77313","full_name":"Muth, Lars","orcid":"0000-0002-2938-5616","last_name":"Muth"},{"id":"21220","full_name":"Sextro, Walter","last_name":"Sextro","first_name":"Walter"},{"first_name":"Peter","full_name":"Eberhard, Peter","last_name":"Eberhard"}],"date_created":"2024-09-11T13:38:03Z","title":"Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction","doi":"https://doi.org/10.1002/pamm.202400027","quality_controlled":"1","year":"2024","citation":{"ama":"Wohlleben MC, Röder B, Ebel H, Muth L, Sextro W, Eberhard P. Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction. <i>PAMM</i>. Published online 2024:e202400027. doi:<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>","ieee":"M. C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, and P. Eberhard, “Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction,” <i>PAMM</i>, p. e202400027, 2024, doi: <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>.","chicago":"Wohlleben, Meike Claudia, Benedict Röder, Henrik Ebel, Lars Muth, Walter Sextro, and Peter Eberhard. “Hybrid Modeling of Multibody Systems: Comparison of Two Discrepancy Models for Trajectory Prediction.” <i>PAMM</i>, 2024, e202400027. <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>.","short":"M.C. Wohlleben, B. Röder, H. Ebel, L. Muth, W. Sextro, P. Eberhard, PAMM (2024) e202400027.","mla":"Wohlleben, Meike Claudia, et al. “Hybrid Modeling of Multibody Systems: Comparison of Two Discrepancy Models for Trajectory Prediction.” <i>PAMM</i>, 2024, p. e202400027, doi:<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>.","bibtex":"@article{Wohlleben_Röder_Ebel_Muth_Sextro_Eberhard_2024, title={Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction}, DOI={<a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>}, journal={PAMM}, author={Wohlleben, Meike Claudia and Röder, Benedict and Ebel, Henrik and Muth, Lars and Sextro, Walter and Eberhard, Peter}, year={2024}, pages={e202400027} }","apa":"Wohlleben, M. C., Röder, B., Ebel, H., Muth, L., Sextro, W., &#38; Eberhard, P. (2024). Hybrid modeling of multibody systems: Comparison of two discrepancy models for trajectory prediction. <i>PAMM</i>, e202400027. <a href=\"https://doi.org/10.1002/pamm.202400027\">https://doi.org/10.1002/pamm.202400027</a>"},"page":"e202400027"},{"doi":"10.36001/phmap.2023.v4i1.3596","main_file_link":[{"open_access":"1","url":"https://www.papers.phmsociety.org/index.php/phmap/article/view/3596"}],"title":"Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System","volume":4,"date_created":"2023-09-18T07:52:32Z","author":[{"first_name":"Osarenren Kennedy","last_name":"Aimiyekagbon","full_name":"Aimiyekagbon, Osarenren Kennedy","id":"9557"},{"last_name":"Löwen","id":"47233","full_name":"Löwen, Alexander","first_name":"Alexander"},{"id":"54290","full_name":"Bender, Amelie","last_name":"Bender","first_name":"Amelie"},{"first_name":"Lars","last_name":"Muth","orcid":"0000-0002-2938-5616","full_name":"Muth, Lars","id":"77313"},{"first_name":"Walter","last_name":"Sextro","id":"21220","full_name":"Sextro, Walter"}],"date_updated":"2024-08-19T07:39:12Z","oa":"1","intvolume":"         4","citation":{"short":"O.K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, W. Sextro, in: Proceedings of the Asia Pacific Conference of the PHM Society 2023 , 2023.","bibtex":"@inproceedings{Aimiyekagbon_Löwen_Bender_Muth_Sextro_2023, title={Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System}, volume={4}, DOI={<a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">10.36001/phmap.2023.v4i1.3596</a>}, number={1}, booktitle={Proceedings of the Asia Pacific Conference of the PHM Society 2023 }, author={Aimiyekagbon, Osarenren Kennedy and Löwen, Alexander and Bender, Amelie and Muth, Lars and Sextro, Walter}, year={2023} }","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System.” <i>Proceedings of the Asia Pacific Conference of the PHM Society 2023 </i>, vol. 4, no. 1, 2023, doi:<a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">10.36001/phmap.2023.v4i1.3596</a>.","apa":"Aimiyekagbon, O. K., Löwen, A., Bender, A., Muth, L., &#38; Sextro, W. (2023). Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System. <i>Proceedings of the Asia Pacific Conference of the PHM Society 2023 </i>, <i>4</i>(1). <a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">https://doi.org/10.36001/phmap.2023.v4i1.3596</a>","ama":"Aimiyekagbon OK, Löwen A, Bender A, Muth L, Sextro W. Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System. In: <i>Proceedings of the Asia Pacific Conference of the PHM Society 2023 </i>. Vol 4. ; 2023. doi:<a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">10.36001/phmap.2023.v4i1.3596</a>","ieee":"O. K. Aimiyekagbon, A. Löwen, A. Bender, L. Muth, and W. Sextro, “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System,” in <i>Proceedings of the Asia Pacific Conference of the PHM Society 2023 </i>, 2023, vol. 4, no. 1, doi: <a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">10.36001/phmap.2023.v4i1.3596</a>.","chicago":"Aimiyekagbon, Osarenren Kennedy, Alexander Löwen, Amelie Bender, Lars Muth, and Walter Sextro. “Expert-Informed Hierarchical Diagnostics of Multiple Fault Modes of a Spacecraft Propulsion System.” In <i>Proceedings of the Asia Pacific Conference of the PHM Society 2023 </i>, Vol. 4, 2023. <a href=\"https://doi.org/10.36001/phmap.2023.v4i1.3596\">https://doi.org/10.36001/phmap.2023.v4i1.3596</a>."},"year":"2023","issue":"1","quality_controlled":"1","language":[{"iso":"eng"}],"keyword":["PHM","Fault Diagnostics","Multiple Fault Modes","Expert-Informed Diagnostics","Anomaly Detection"],"department":[{"_id":"151"}],"user_id":"9557","_id":"47116","status":"public","abstract":[{"lang":"eng","text":"This paper presents a comprehensive study on diagnosing a spacecraft propulsion system utilizing data provided by the Prognostics and Health Management (PHM) society, specifically obtained as part of the Asia-Pacific PHM conference’s data challenge 2023. The objective of the challenge is to identify and diagnose known faults as well as unknown anomalies in the spacecraft’s propulsion system, which is critical for ensuring the spacecraft’s proper functionality and safety. To address this challenge, the proposed method follows a systematic approach of feature extraction, feature selection, and model development. The models employed in this study are kMeans clustering and decision trees combined to ensembles, enriched with expert knowledge. With the method presented, our team was capable of reaching high accuracy in identifying anomalies as well as diagnosing faults, resulting in attaining the seventh place with a score of 93.08 %."}],"publication":"Proceedings of the Asia Pacific Conference of the PHM Society 2023 ","type":"conference"},{"publication_status":"published","publication_identifier":{"issn":["1617-7061","1617-7061"]},"quality_controlled":"1","citation":{"bibtex":"@inproceedings{Wohlleben_Muth_Peitz_Sextro_2023, title={Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits}, DOI={<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>}, booktitle={Proceedings in Applied Mathematics and Mechanics}, publisher={Wiley}, author={Wohlleben, Meike Claudia and Muth, Lars and Peitz, Sebastian and Sextro, Walter}, year={2023} }","short":"M.C. Wohlleben, L. Muth, S. Peitz, W. Sextro, in: Proceedings in Applied Mathematics and Mechanics, Wiley, 2023.","mla":"Wohlleben, Meike Claudia, et al. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.” <i>Proceedings in Applied Mathematics and Mechanics</i>, Wiley, 2023, doi:<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>.","apa":"Wohlleben, M. C., Muth, L., Peitz, S., &#38; Sextro, W. (2023). Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. <i>Proceedings in Applied Mathematics and Mechanics</i>. <a href=\"https://doi.org/10.1002/pamm.202300039\">https://doi.org/10.1002/pamm.202300039</a>","ieee":"M. C. Wohlleben, L. Muth, S. Peitz, and W. Sextro, “Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits,” 2023, doi: <a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>.","chicago":"Wohlleben, Meike Claudia, Lars Muth, Sebastian Peitz, and Walter Sextro. “Transferability of a Discrepancy Model for the Dynamics of Electromagnetic Oscillating Circuits.” In <i>Proceedings in Applied Mathematics and Mechanics</i>. Wiley, 2023. <a href=\"https://doi.org/10.1002/pamm.202300039\">https://doi.org/10.1002/pamm.202300039</a>.","ama":"Wohlleben MC, Muth L, Peitz S, Sextro W. Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits. In: <i>Proceedings in Applied Mathematics and Mechanics</i>. Wiley; 2023. doi:<a href=\"https://doi.org/10.1002/pamm.202300039\">10.1002/pamm.202300039</a>"},"year":"2023","date_created":"2023-09-06T05:18:05Z","author":[{"orcid":"0009-0009-9767-7168","last_name":"Wohlleben","id":"43991","full_name":"Wohlleben, Meike Claudia","first_name":"Meike Claudia"},{"first_name":"Lars","last_name":"Muth","orcid":"0000-0002-2938-5616","id":"77313","full_name":"Muth, Lars"},{"full_name":"Peitz, Sebastian","id":"47427","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"publisher":"Wiley","date_updated":"2023-09-21T14:47:20Z","oa":"1","main_file_link":[{"url":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/pamm.202300039","open_access":"1"}],"doi":"10.1002/pamm.202300039","title":"Transferability of a discrepancy model for the dynamics of electromagnetic oscillating circuits","type":"conference","publication":"Proceedings in Applied Mathematics and Mechanics","status":"public","abstract":[{"text":"Modelling of dynamic systems plays an important role in many engineering disciplines. Two different approaches are physical modelling and data‐driven modelling, both of which have their respective advantages and disadvantages. By combining these two approaches, hybrid models can be created in which the respective disadvantages are mitigated, with discrepancy models being a particular subclass. Here, the basic system behaviour is described physically, that is, in the form of differential equations. Inaccuracies resulting from insufficient modelling or numerics lead to a discrepancy between the measurements and the model, which can be compensated by a data‐driven error correction term. Since discrepancy methods still require a large amount of measurement data, this paper investigates the extent to which a single discrepancy model can be trained for a physical model with additional parameter dependencies without the need for retraining. As an example, a damped electromagnetic oscillating circuit is used. The physical model is realised by a differential equation describing the electric current, considering only inductance and capacitance; dissipation due to resistance is neglected. This creates a discrepancy between measurement and model, which is corrected by a data‐driven model. In the experiments, the inductance and the capacity are varied. It is found that the same data‐driven model can only be used if additional parametric dependencies in the data‐driven term are considered as well.","lang":"eng"}],"user_id":"77313","department":[{"_id":"655"},{"_id":"151"}],"_id":"46813","language":[{"iso":"eng"}],"keyword":["Electrical and Electronic Engineering","Atomic and Molecular Physics","and Optics"]},{"publication_identifier":{"eisbn":["978-3-031-07305-2"],"isbn":["978-3-031-07304-5"]},"publication_status":"published","place":"Cham","citation":{"ama":"Muth L, Noll C, Sextro W. Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In: Orlova A, Cole D, eds. <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021</i>. Lecture Notes in Mechanical Engineering. Springer; 2022. doi:<a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">10.1007/978-3-031-07305-2_92</a>","ieee":"L. Muth, C. Noll, and W. Sextro, “Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data,” in <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021</i>, Saint Petersburg, Russia, 2022, doi: <a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">10.1007/978-3-031-07305-2_92</a>.","chicago":"Muth, Lars, Christian Noll, and Walter Sextro. “Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data.” In <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021</i>, edited by Anna Orlova and David Cole. Lecture Notes in Mechanical Engineering. Cham: Springer, 2022. <a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">https://doi.org/10.1007/978-3-031-07305-2_92</a>.","apa":"Muth, L., Noll, C., &#38; Sextro, W. (2022). Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In A. Orlova &#38; D. Cole (Eds.), <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021</i>. Springer. <a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">https://doi.org/10.1007/978-3-031-07305-2_92</a>","bibtex":"@inproceedings{Muth_Noll_Sextro_2022, place={Cham}, series={Lecture Notes in Mechanical Engineering}, title={Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data}, DOI={<a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">10.1007/978-3-031-07305-2_92</a>}, booktitle={Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021}, publisher={Springer}, author={Muth, Lars and Noll, Christian and Sextro, Walter}, editor={Orlova, Anna and Cole, David}, year={2022}, collection={Lecture Notes in Mechanical Engineering} }","short":"L. Muth, C. Noll, W. Sextro, in: A. Orlova, D. Cole (Eds.), Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021, Springer, Cham, 2022.","mla":"Muth, Lars, et al. “Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data.” <i>Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021</i>, edited by Anna Orlova and David Cole, Springer, 2022, doi:<a href=\"https://doi.org/10.1007/978-3-031-07305-2_92\">10.1007/978-3-031-07305-2_92</a>."},"date_updated":"2022-08-23T11:55:07Z","author":[{"first_name":"Lars","full_name":"Muth, Lars","id":"77313","last_name":"Muth","orcid":"0000-0002-2938-5616"},{"last_name":"Noll","full_name":"Noll, Christian","first_name":"Christian"},{"last_name":"Sextro","id":"21220","full_name":"Sextro, Walter","first_name":"Walter"}],"doi":"10.1007/978-3-031-07305-2_92","conference":{"start_date":"2021-08-17","name":"27th IAVSD Symposium on Dynamics of Vehicles on Roads and Tracks, IAVSD 2021","location":"Saint Petersburg, Russia","end_date":"2021-08-19"},"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007/978-3-031-07305-2_92"}],"type":"conference","editor":[{"last_name":"Orlova","full_name":"Orlova, Anna","first_name":"Anna"},{"first_name":"David","full_name":"Cole, David","last_name":"Cole"}],"status":"public","_id":"29934","department":[{"_id":"151"}],"user_id":"77313","series_title":"Lecture Notes in Mechanical Engineering","quality_controlled":"1","year":"2022","publisher":"Springer","date_created":"2022-02-21T14:14:11Z","title":"Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data","publication":"Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021","abstract":[{"lang":"eng","text":"Tire and road wear are a major source of emissions of nonexhaust particulate matter (PM) and make up the largest share of microplastics in the environment. To reduce tire wear through numerical optimization of a vehicle's suspension system, fast simulations of the representative usage of a vehicle are needed. Therefore, this contribution evaluates if instead of a full simulation of a representative test drive, only specific driving maneuvers resulting from a clustering of the driving data can be used to predict tire wear. As a measure for tire wear, the friction work between tire and road is calculated. It is shown that enough clusters result in negligible deviations between the total friction work of the full simulation and the cluster simulations as well as between the distributions of the friction work over the tire width. The calculation time can be reduced to about 1% of the full simulation."}],"keyword":["Tire Wear","Vehicle Dynamics","Clustering","Virtual Test"],"language":[{"iso":"eng"}]},{"type":"conference","status":"public","editor":[{"first_name":"Phuc","last_name":"Do","full_name":"Do, Phuc"},{"full_name":"King, Steve","last_name":"King","first_name":"Steve"},{"first_name":"Olga","last_name":"Fink","full_name":"Fink, Olga"}],"user_id":"9557","department":[{"_id":"151"}],"_id":"27111","publication_status":"published","citation":{"short":"O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P. Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021, 2021, pp. 527–536.","mla":"Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production Line.” <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>.","bibtex":"@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based Diagnostics of a Production Line}, volume={6}, DOI={<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>}, number={1}, booktitle={Proceedings of the European Conference of the PHM Society 2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve and Fink, Olga}, year={2021}, pages={527–536} }","apa":"Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &#38; Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &#38; O. Fink (Eds.), <i>Proceedings of the European Conference of the PHM Society 2021</i> (Vol. 6, Issue 1, pp. 527–536). <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>","ama":"Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics of a Production Line. In: Do P, King S, Fink O, eds. <i>Proceedings of the European Conference of the PHM Society 2021</i>. Vol 6. ; 2021:527-536. doi:<a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>","chicago":"Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In <i>Proceedings of the European Conference of the PHM Society 2021</i>, edited by Phuc Do, Steve King, and Olga Fink, 6:527–36, 2021. <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">https://doi.org/10.36001/phme.2021.v6i1.3042</a>.","ieee":"O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based Diagnostics of a Production Line,” in <i>Proceedings of the European Conference of the PHM Society 2021</i>, 2021, vol. 6, no. 1, pp. 527–536, doi: <a href=\"https://doi.org/10.36001/phme.2021.v6i1.3042\">10.36001/phme.2021.v6i1.3042</a>."},"intvolume":"         6","page":"527-536","author":[{"first_name":"Osarenren Kennedy","last_name":"Aimiyekagbon","full_name":"Aimiyekagbon, Osarenren Kennedy","id":"9557"},{"id":"77313","full_name":"Muth, Lars","last_name":"Muth","orcid":"0000-0002-2938-5616","first_name":"Lars"},{"orcid":"0009-0009-9767-7168","last_name":"Wohlleben","id":"43991","full_name":"Wohlleben, Meike Claudia","first_name":"Meike Claudia"},{"id":"54290","full_name":"Bender, Amelie","last_name":"Bender","first_name":"Amelie"},{"full_name":"Sextro, Walter","id":"21220","last_name":"Sextro","first_name":"Walter"}],"volume":6,"date_updated":"2023-09-22T09:13:01Z","oa":"1","main_file_link":[{"open_access":"1","url":"http://papers.phmsociety.org/index.php/phme/article/download/3042/1812"}],"conference":{"name":"PHM Society European Conference"},"doi":"10.36001/phme.2021.v6i1.3042","publication":"Proceedings of the European Conference of the PHM Society 2021","abstract":[{"lang":"eng","text":"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."}],"language":[{"iso":"eng"}],"keyword":["PHME 2021","Feature Selection Classification","Feature Selection Clustering","Interpretable Model","Transparent Model","Industry 4.0","Real-World Diagnostics","Quality Control","Predictive Maintenance"],"issue":"1","quality_controlled":"1","year":"2021","date_created":"2021-11-03T12:26:39Z","title":"Rule-based Diagnostics of a Production Line"}]