An approach for mastering data-induced conflicts in the digital twin context

Abstract

Decision-making highly relies on the accuracy and veracity of data. Therefore, redundant data acquisition and fusion has established but lack the ability to handle conflicting data correctly. Especially digital twins, which complement physical products with mathematical models, and contribute to redundancy. Uncertainty propagates through the digital twin and provides the opportunity to check data for conflicts, to identify affected subsystems and to infer a possible cause. This work presents an approach that combines a digital twin with the ability of uncertainty propagation, conflict detection, processing and visualisation techniques for mastering data-induced conflicts. The capability of this method to identify and isolate faults was examined on a technical system with a multitude of sensors.