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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.

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Moderne Unternehmen im produzierenden Gewerbe stellen sich täglich den Herausforderungen des Marktes und der globalen Wirtschaft. Ein Schlüssel zum Erfolg ist die smarte Produktion. Eine Aufgabe, die es zur Einführung einer smarten Produktion zu bewältigen gilt, stellt die Einbindung von Bestandsanlagen in einen Industrie-4.0-Kontext dar. In diesem Artikel werden die wesentlichen Schritte und datenbasierten Ansätze beschrieben, um Bestandsanlagen in eine smarte Produktion einzubetten. Dazu zählen die Datenerfassung, der Einsatz von maschinellen Lernverfahren sowie die Abbildung von Expertenwissen mit einem Random Forest.

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