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Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge

T. Voigt, M. Kohlhase, O. Nelles, Mathematics 9 (2021).

Artikel | Veröffentlicht | Englisch
Autor*in
Voigt, TimFH Bielefeld; Kohlhase, MartinFH Bielefeld ; Nelles, Oliver
Abstract
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.
Erscheinungsjahr
Zeitschriftentitel
Mathematics
Band
9
Zeitschriftennummer
19
Artikelnummer
2479
eISSN
FH-PUB-ID

Zitieren

Voigt, Tim ; Kohlhase, Martin ; Nelles, Oliver: Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. In: Mathematics Bd. 9, MDPI AG (2021), Nr. 19
Voigt T, Kohlhase M, Nelles O. Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics. 2021;9(19). doi:10.3390/math9192479
Voigt, T., Kohlhase, M., & Nelles, O. (2021). Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge. Mathematics, 9(19). https://doi.org/10.3390/math9192479
@article{Voigt_Kohlhase_Nelles_2021, title={Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge}, volume={9}, DOI={10.3390/math9192479}, number={192479}, journal={Mathematics}, publisher={MDPI AG}, author={Voigt, Tim and Kohlhase, Martin and Nelles, Oliver}, year={2021} }
Voigt, Tim, Martin Kohlhase, and Oliver Nelles. “Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge.” Mathematics 9, no. 19 (2021). https://doi.org/10.3390/math9192479.
T. Voigt, M. Kohlhase, and O. Nelles, “Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge,” Mathematics, vol. 9, no. 19, 2021.
Voigt, Tim, et al. “Incremental DoE and Modeling Methodology with Gaussian Process Regression: An Industrially Applicable Approach to Incorporate Expert Knowledge.” Mathematics, vol. 9, no. 19, 2479, MDPI AG, 2021, doi:10.3390/math9192479.
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