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An Architectural Design for Measurement Uncertainty Evaluation in Cyber-Physical Systems

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Document pages: 5 pages

Abstract: Several use cases from the areas of manufacturing and process industry,require highly accurate sensor data. As sensors always have some degree ofuncertainty, methods are needed to increase their reliability. The commonapproach is to regularly calibrate the devices to enable traceability accordingto national standards and Système international (SI) units - which followscostly processes. However, sensor networks can also be represented as CyberPhysical Systems (CPS) and a single sensor can have a digital representation(Digital Twin) to use its data further on. To propagate uncertainty in areliable way in the network, we present a system architecture to communicatemeasurement uncertainties in sensor networks utilizing the concept of AssetAdministration Shells alongside methods from the domain of Organic Computing.The presented approach contains methods for uncertainty propagation as well asconcepts from the Machine Learning domain that combine the need for an accurateuncertainty estimation. The mathematical description of the metrologicaluncertainty of fused or propagated values can be seen as a first step towardsthe development of a harmonized approach for uncertainty in distributed CPSs inthe context of Industrie 4.0. In this paper, we present basic use cases,conceptual ideas and an agenda of how to proceed further on.

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