Fogification of electric drives: An industrial use case

Electric drives are used to control electric motors, which are pervasive in industrial applications. In this paper we propose enhancing the electric drives to fulfil the role of fog nodes within a Fog Computing Platform (FCP). Fog Computing is envisioned as a realization of future distributed archit...

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Published in2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) Vol. 1; pp. 77 - 84
Main Authors Barzegaran, Mohammadreza, Desai, Nitin, Qian, Jia, Tange, Koen, Zarrin, Bahram, Pop, Paul, Kuusela, Juha
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2020
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Summary:Electric drives are used to control electric motors, which are pervasive in industrial applications. In this paper we propose enhancing the electric drives to fulfil the role of fog nodes within a Fog Computing Platform (FCP). Fog Computing is envisioned as a realization of future distributed architectures in Industry 4.0. We identify the system-level requirements of such an FCP, including requirements that are extracted from the current architecture of drives, which we consider as a baseline. These requirements are then used to design a system-level architecture, which we model using the Architecture Analysis & Design Language (AADL). We identify the "technology bricks" (components such as hardware, software, middleware, services, methods and tools) needed to implement the FCP. The proposed fog-based architecture is then used to implement a Conveyor Belt industrial use case. We evaluate the resulting use case on several aspects, demonstrating the usefulness of the proposed fog-based approach. By developing the electric drives as fog nodes, that we call fogification, new offerings like programmability, analytics and connectivity to customer Clouds are expected to increase the added value. Increased flexibility allows drives to assume a larger role in industrial and domestic control systems, instrumenting thus also legacy systems by using drives as the data source.
ISSN:1946-0759
DOI:10.1109/ETFA46521.2020.9212010