The digital transformation that is on-going worldwide, and triggered by the Industry 4.0 initiative, has brought to the surface new concepts and emergent technologies. One of these new concepts is the Digital Twin, which recently started gaining momentum, and is related to creating a virtual copy of the physical system, providing a connection between the real and virtual systems to collect and analyze and simulate data in the virtual model to improve the performance of the real system. The benefits of using the digital twin approach is attracting significant attention and interest from research and industry communities in the last few years, and its importance will increase in the upcoming years. Having this in mind, this paper surveys and discusses the digital twin concept in the context of the 4th industrial revolution, particularly focusing the concept and functionalities, the associated technologies, the industrial applications and the research challenges. The applicability of the digital concept is illustrated by the virtualisation of an UR3 collaborative robot which used the V-REP simulation environment and the Modbus communication protocol.

In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.