TY - CONF AU - Voigt, Tim AU - Schöne, Marvin AU - Kohlhase, Martin AU - Nelles, Oliver AU - Kuhn, Martin ED - Yin, Hujun ED - Camacho, David ED - Tino, Peter ID - 2232 SN - 0302-9743 T2 - Intelligent Data Engineering and Automated Learning – IDEAL 2022. 23rd International Conference, IDEAL 2022, Manchester, UK, November 24–26, 2022, Proceedings TI - Using Design of Experiments to Support the Commissioning of Industrial Assembly Processes ER - TY - CHAP AU - Hanitz, Marcel AU - Schöne, Marvin AU - Voigt, Tim AU - Kohlhase, Martin ED - Perner, Petra ID - 2291 SN - 978-3-942952-93-4 T2 - Machine Learning and Data Mining in Pattern Recognition, MLDM 2022 TI - Analysis of the Behavior of Online Decision Trees Under Concept Drift at the Example of FIMT-DD ER - TY - CONF AU - Voigt, Tim AU - Schöne, Marvin AU - Kohlhase, Martin AU - Nelles, Oliver AU - Kuhn, Martin ID - 3718 T2 - 2021 3rd International Conference on Management Science and Industrial Engineering TI - Space-Filling Designs for Experiments with Assembled Products ER - TY - JOUR AB - The use of data-based models is a favorable way to optimize existing industrial processes. Estimation of these models requires data with sufficient information content. However, data from regular process operation are typically limited to single operating points, so industrially applicable design of experiments (DoE) methods are needed. This paper presents a stepwise DoE and modeling methodology, using Gaussian process regression that incorporates expert knowledge. This expert knowledge regarding an appropriate operating point and the importance of various process inputs is exploited in both the model construction and the experimental design. An incremental modeling scheme is used in which a model is additively extended by another submodel in a stepwise fashion, each estimated on a suitable experimental design. Starting with the most important process input for the first submodel, the number of considered inputs is incremented in each step. The strengths and weaknesses of the methodology are investigated, using synthetic data in different scenarios. The results show that a high overall model quality is reached, especially for processes with few interactions between the inputs and low noise levels. Furthermore, advantages in the interpretability and applicability for industrial processes are discussed and demonstrated, using a real industrial use case as an example. AU - Voigt, Tim AU - Kohlhase, Martin AU - Nelles, Oliver ID - 3717 IS - 19 JF - Mathematics KW - Gaussian process regression KW - design of experiments KW - static process models KW - industrial processes KW - stepwise experimental design TI - Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge VL - 9 ER - TY - CONF AU - Voigt, Tim AU - Migenda, Nico AU - Schöne, Marvin AU - Pelkmann, David AU - Fricke, Matthias AU - Schenck, Wolfram AU - Kohlhase, Martin ID - 2571 T2 - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) TI - Advanced Data Analytics Platform for Manufacturing Companies ER - TY - CONF AU - Steinmann, Luca AU - Migenda, Nico AU - Voigt, Tim AU - Kohlhase, Martin AU - Schenck, Wolfram ID - 2572 SN - 9781450388887 T2 - 2021 3rd International Conference on Management Science and Industrial Engineering TI - Variational Autoencoder based Novelty Detection for Real-World Time Series ER - TY - CONF AU - Voigt, Tim AU - Kohlhase, Martin AU - Nelles, Oliver ID - 1367 T2 - 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) TI - Incremental Latin Hypercube Additive Design for LOLIMOT ER - TY - JOUR AU - Voigt, Tim AU - Kohlhase, Martin AU - Peter, Armin ID - 1368 IS - 04 JF - atp magazin TI - Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels VL - 04 ER - TY - CONF AU - Voigt, Tim AU - Kohlhase, Martin AU - Nelles, Oliver ID - 1371 T2 - Proceedings - 29. Workshop Computational Intelligence TI - Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs ER - TY - CONF AU - Voigt, Tim AU - Kohlhase, Martin ID - 1369 T2 - Proceedings - 28. Workshop Computational Intelligence TI - Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern ER -