Device recognition assistants as additional data management method for Digital Twins

Abstract

Reusing shopfloor data within Digital Twins offers the potential to optimize manufacturing-, production planning-, and engineering processes. Reusing data is only possible if the data is structured independently of the involved project teams. Current approaches focus on valuable, but complex standardization initiatives. This paper introduces device recognition assistants as a new, flexible data management method for Digital Twins. These assistants are based on time series classifiers to structure the communicated raw data. The assistants are experimentally evaluated. The results show the large potential for semantic annotations via time series classifications to assist the reuse of data within Digital Twins.