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Design considerations introducing analytics as a “dual use” in complex industrial embedded systems
Hitachi-ABB Power Grids, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-3375-6766
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-0921-0878
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-6657-2496
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2021 (English)In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), 2021Conference paper, Published paper (Refereed)
Abstract [en]

Embedded systems are today often self-sufficient with limited and predefined communication. However, this traditional view of embedded systems is changing through advancements in technologies such as, communication, cloud technologies, and advanced analytics including machine learning. These advancements have increased the benefits of building Systems of Systems (SoS) that can provide a functionality with unique capabilities that none of the included subsystems can accomplish separately. By this gain of functionality the embedded system is evolving towards a “dual use” purpose<sup>1</sup><sup>1</sup>In this paper we define dual usage as a control system having two purposes. In other contexts such as politics, diplomacy and export control, the term “dual-use” refers to technology that can be used for both peaceful and military aims, e.g., nuclear power technology., The use is dual in the sense that the system still needs to handle its original task, e.g., control and protect of an asset, and it must provide information for creating the SoS. Larger installations, e.g., industry plants, power systems and generation, have in most cases a long expected life-cycle, some up to 30–40 years without significant updates, compared to analytical functions that evolve and change much faster, i.e., requiring new types of data sets from the subsystems, not know at its first deployment. This difference in development cycles calls for new solutions supporting updates related to new requirements inherent in analytical functions. In this paper, within the context of “dual usage” of systems and subsystems, we analyze the impact on an embedded system, new or legacy, when it is required to provide analytic data with high quality. We compare a reference system, implementing all functions in one CPU core, to three other alternative solutions: a) a multi-core system where we are using a separate core for analytics, b) using a separate analytics CPU and c) analytics functionality located in a separate subsystem. Our conclusion is that the choice of analytics information collection method should to be based on intended usage, along with resulting complexity and cost of updates compared to hardware cost.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Industries, Embedded systems, Costs, Multicore processing, Machine learning, Hardware, Power systems, systems-of-systems, analytics, data gathering, data collection, long life time
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-57444DOI: 10.1109/ETFA45728.2021.9613273OAI: oai:DiVA.org:ri-57444DiVA, id: diva2:1623281
Conference
2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA).7-10 Sept. 2021
Available from: 2021-12-28 Created: 2021-12-28 Last updated: 2021-12-28Bibliographically approved

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Sandström, KristianLarsson, StigEricsson, Niclas

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