Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0

Oks SJ, Zöllner S, Jalowski M, Fuchs J, Möslein K (2021)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2021

Journal

Procedia CIRP Curran

Book Volume: 100

Pages Range: 43-48

URI: https://www.sciencedirect.com/science/article/pii/S2212827121004650

DOI: 10.1016/j.procir.2021.05.007

Open Access Link: https://www.sciencedirect.com/science/article/pii/S2212827121004650

Abstract

Sensor application is a basis for digitized industrial value creation. However, for existing production and logistics systems, sensor retrofitting is accompanied by challenges, including plant heterogeneity and lack of standards. This work addresses this issue through the design, implementation and evaluation of an embedded vision high-bay shelf monitoring system of an Industry 4.0 demonstrator. Utilizing design science research methodologies, the artifact unites the concepts of computer vision, convolutional neural networks and OPC UA for widely applicable and cost-efficient retrofitting. Design principles derived from the artifact’s design and evaluation cycles can serve as abstracted guidelines for designing retrofit visual sensor systems.

Authors with CRIS profile

Sascha Julian Oks Chair of Information Systems I, Innovation and Value Creation Max Jalowski Chair of Information Systems I, Innovation and Value Creation Jonathan Fuchs Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Kathrin Möslein Chair of Information Systems I, Innovation and Value Creation

Related research project(s)

Methodische Konzeptentwicklung für Dienstleistungsplattformen und Schnittstellen, Service Systems Engineering, Kommunikationsdesign (PRODISYS) Engineering Produktionsbezogener Dienstleistungssysteme July 1, 2017 - Dec. 31, 2020

How to cite

APA:

Oks, S.J., Zöllner, S., Jalowski, M., Fuchs, J., & Möslein, K. (2021). Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0. Procedia CIRP, 100, 43-48. https://doi.org/10.1016/j.procir.2021.05.007

MLA:

Oks, Sascha Julian, et al. "Embedded vision device integration via OPC UA: Design and evaluation of a neural network-based monitoring system for Industry 4.0." Procedia CIRP 100 (2021): 43-48.

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