A data model for linking testbed and field test data

Sauer C, Schleich B, Wartzack S (2021)

Publication Type: Conference contribution, Original article

Publication year: 2021

Original Authors: Benjamin Schleich Christopher Sauer

Event location: Online DE

DOI: 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

Authors with CRIS profile

Christopher Sauer Chair of Engineering Design (KTmfk) Benjamin Schleich Chair of Engineering Design (KTmfk) Sandro Wartzack Chair of Engineering Design (KTmfk)

Related research project(s)

Customized Digital Engineering für bayerische KMU am Beispiel des Antriebsstrangs elektrischer Fahrzeuge (FORCuDE@BEV) April 1, 2020 - March 31, 2023

How to cite

APA:

Sauer, C., Schleich, B., & Wartzack, S. (2021). A data model for linking testbed and field test data. In Proceedings of the 32nd Symposium Design for X (DFX2021). Online, DE.

MLA:

Sauer, Christopher, Benjamin Schleich, and Sandro Wartzack. "A data model for linking testbed and field test data." Proceedings of the 32nd Symposium Design for X (DFX2021), Online 2021.

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