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Creation of Digital Twins by Combining Fuzzy Rules with Artificial Neural Networks

  • The rise of digital twins in the manufacturing industry is accompanied by new possibilities, like process automation and condition monitoring, real time simulations and quality and maintenance prediction are just a few advantages which can be realized. This paper takes a novel approach by extracting the fundamental knowledge of a data set from a production process and mapping it to an expert fuzzy rule set. Afterwards, new fundamental augmented data is generated by exploring the feature space of the previously generated fuzzy rule set. At the same time, a high number of artificial neural network (ANN)models with different hyperparameter configurations are created. The best models are chosen, in line with the idea of survival of the fittest, and improved with the additional training data sets, generated by the fuzzy rule simulation. It is shown that ANN models can be improved by adding fundamental knowledge represented by the discovered fuzzy rules. Those models can represent digitized machines as digital twins. The architecture and effectiveness of the digital twin is evaluated within an industry 4.0 use case.

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Metadaten
Author:Matthias Lermer, Christoph ReichGND
DOI:https://doi.org/10.1109/IECON.2019.8926914
ISBN:978-1-7281-4878-6
Parent Title (German):IECON 2019 : 45th Annual Conference of the IEEE Industrial Electronics Society, 14 - 17 October, 2019, Lisbon, Portugal; Proceedings
Document Type:Conference Proceeding
Language:English
Year of Completion:2019
Release Date:2020/01/28
Tag:Artificial neural networks; Digital twin; Fuzzy logic; Industry 4.0
First Page:5849
Last Page:5854
Open Access:Innerhalb der Hochschule
Licence (German):License LogoEs gilt das UrhG