Utilizing Digital Twin to reduce fault diagnosis time of a reconfigurable machine built on the concepts of Industry 4.0

Abstract

Real-time implementation of Industry 4.0 concepts often emphasizes the integration of cross-platform technologies synchronized to manufacture one determined product. A digital twin replicates physical entities, adding a virtual model to visualize the real-time operations of a production plant. Such revolutionizing concepts of Industry 4.0 and emerging relevant technologies optimize the production efficiency in terms of swift fault detection of machines. To be specific, the production facilities that intend to deliver mass-customized products using reconfigurable machines often face extended downtime due to their complex operation. This innovative research work employs interoperable controllers that perform data-driven execution, receiving job requests from cross-platform social media to produce customized products (i.e., flavoured yogurt). Practically, when such machinery continuously manufactures customized products and stops working due to an unknown reason, a subject matter expert (SME) is often required to diagnose the error or detect faults in each domain. This leads to extended downtime, reducing Overall Equipment Effectiveness (OEE). In this paper, we explain the usage of digital twin technology built using Factory IO to reduce machine downtime, fasten fault diagnoses, and increase OEE, which ensures availability, performance, and quality.