In order to assign measured values a quantitative statement about their reliability, the metrological quality infrastructure for traditional industrial measurement and calibration procedures is based on accredited calibration facilities and standardized evaluation procedures. In the course of the introduction of Industry 4.0 technologies, the basic procedures for this are to be revised in order to be able to automatically determine the quality of measurement data even in changing systems. For this purpose, the sensors are to be technically connected to a digital twin in the form of an Asset Administration Shell. This digital twin will then be able to communicate information about the measurement uncertainty. Furthermore, sub-networks of sensors are to be combined in flexible mathematical models to enable machine-oriented data evaluation. For this purpose, methods of organic computing will be investigated in order to establish the flexible and partly autonomously acting sub-networks. Furthermore, a methodology is to be developed to identify uncertain measuring points from aggregated measured values or characteristic values.
