Digital Twin Applied to Predictive Maintenance for Industry 4.0

Abstract

The major concept of the future Industrial 4.0 framework is the integration of artificial intelligence (AI) and the implementation of digital twin (DT), which avoids serious economic losses caused by unexpected equipment failures and significantly improves system reliability. DT is an emerging technology in the context of digital transformation that enables the monitoring, diagnosis, energy efficiency, and optimization of different systems. Numerous initiatives have shown how AI can enhance the performance of DT for industrial applications. This paper describes a data-based DT architecture for the monitoring, and predictive maintenance (PdM) in manufacturing. This new concept is based on deep learning, specifically the autoencoder model. The system was tested on a real industry example, by developing the data collection, data system analysis, and applying the deep learning approach. The data were collected from a Profinet communication network installed on an automated system. This approach enables better quality results and more efficient management of the weaver's workshop. Lastly, to prove the efficiency and the accuracy of the newly developed approach, an example is shown.