A digital twin-driven industrial context-aware system: A case study of overhead crane operation

Abstract

With advancements in Information and Communication Technologies (ICT), traditional manufacturing industries are engaged in a digital transformation. This transformation enables the acquisition of vast amounts of data and information, enhancing decision-making capabilities. This, in turn, has raised the expectations of field operators who seek data and information management tailored to the dynamic working environment, thereby improving efficiency in their daily operations. However, there is a lack of a holistic approach to integrating diverse data sources, extracting valuable contextual information, and delivering real-time information to field operators. This paper addresses this gap by proposing an adaptive, interoperable, and user-centered Context-Aware System (CAS). Initially, the paper explores the challenges and requirements associated with CAS’s current practices while proposing potential solutions. Furthermore, it introduces a system framework of CAS that integrates Digital Twin (DT) and semantic technologies. This framework includes three primary technical solutions: (1) Integrating DT to create a comprehensive digital representation of physical entities, enabling real-time data integration and synchronization; (2) Providing an ontology-based approach to model manufacturing context, facilitating knowledge representation and reasoning; (3) Developing a user-centered information delivery system leveraging Augmented Reality (AR) for context-aware visualization. The system architecture has been implemented and tested in a laboratory-scale industrial environment, focusing on crane operations within logistics scenarios. Lastly, three use cases are presented to demonstrate the system’s practical applicability, showcasing its feasibility in furnishing informed contextual information to end-users within the dynamic manufacturing environment.