A data model for linking testbed and field test data

Proceedings of the 32nd Symposium Design for X (DFX2021)

Year: 2021
Editor: Dieter Krause, Kristin Paetzold, Sandro Wartzack
Author: Christopher Sauer, Benjamin Schleich, Sandro Wartzack
Series: DfX
Institution: Lehrstuhl für Konstruktionstechnik (KTmfk), Friedrich-Alexander-Universität Erlangen-Nürnberg
Section: Digital Engineering
Page(s): 10
DOI number: 10.35199/dfx2021.01

Abstract

With the help of data-driven methods such as machine learning, the development of the current product generation can be supported and improved through the early use of data from previous products and product generations. For example, machine learning can be used to predict later product behaviour in field tests from testbed data. This can significantly shorten the development time and save expensive field tests. To implement this data provision for the development processes, uniform data models enable the use of data-driven methods and are of central importance. This paper presents a data model using the example of a testbed for electric vehicle transmissions. Here, potentials for a later data-driven prediction of the product behaviour in the field test for the optimisation of the existing development are shown

Keywords: Graph databases, machine learning, digital engineering

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