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

Abstract

Several use cases from the areas of manufacturing and process industry, require highly accurate sensor data. As sensors always have some degree of uncertainty, methods are needed to increase their reliability. The common approach is to regularly calibrate the devices to enable traceability according to national standards and Système International (SI) unitswhich follows costly processes. However, sensor networks can also be represented as Cyber Physical Systems (CPS) and a single sensor can have a digital representation (Digital Twin) to use its data further on. To propagate uncertainty in a reliable way in the network, we present a system architecture to communicate measurement uncertainties in sensor networks utilizing the concept of Asset Administration Shells alongside methods from the domain of Organic Computing. The presented approach contains methods for uncertainty propagation as well as concepts from the Machine Learning domain that combine the need for an accurate uncertainty estimation. The mathematical description of the metrological uncertainty of fused or propagated values can be seen as a first step towards the development of a harmonized approach for uncertainty in distributed CPS in the context of Industrie 4.0. In this paper, we present basic use cases, conceptual ideas and an agenda of how to proceed further on.

Verification of Industrial Temperature Measurements Using a Non-Invasive Approach

“…In the context of digitalization and Industry 4.0, new insights and opportunities are reliant not only on more measurement points but also on increased quality of the measurement [13,14]. Considering the costs of verification and calibration, it is currently only applied to critically important measurement points.…”