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

Tim Voigt, Martin Kohlhase and Oliver Nelles
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Tim Voigt: Center for Applied Data Science Gütersloh, Faculty of Engineering and Mathematics, University of Applied Sciences, 33619 Bielefeld, Germany
Martin Kohlhase: Center for Applied Data Science Gütersloh, Faculty of Engineering and Mathematics, University of Applied Sciences, 33619 Bielefeld, Germany
Oliver Nelles: Automatic Control—Mechatronics, University of Siegen, 57076 Siegen, Germany

Mathematics, 2021, vol. 9, issue 19, 1-26

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.

Keywords: Gaussian process regression; design of experiments; static process models; industrial processes; stepwise experimental design (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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