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Digital twin (DT) is an emerging technology in the context of digital transformation that enables the monitoring, diagnosis, energy efficiency, and optimization of different systems. The model of DT is a crucial feature for an accurate representation of the physical system. The latter can be complex and dynamic which makes it prone to variability and stochastic behavior. Thus, monitoring through a DT model that gives as an output a single best estimation of the nominal behavior can sometimes be insufficient considering the dynamic properties of the system. For this reason, the current paper intends to present a novel approach for DT modeling through interval models to bound and include the uncertainties inside the model using a statistical approach and Hilbert transform. A case study is presented focusing on the energy consumption of an industrial robot considering the variability of the real process and the measurement noise.

Digital Twin (DT) is one of the promising digital technologies being developed at present to support digital transformation. Successful execution of DT requires real-time access to the collected data. However, real-time connection and synchronization are a significant difficulty owing to the physical environment's unique characteristics of the physical environment such as variability, uncertainty, the different scales of the physical and virtual spaces, and the data generated continuously by various entities. Prior research has been conducted to address the DT synchronization problem in the literature. However, there is a lack of standard approaches or mechanisms for digital twins at the field level in manufacturing systems. Hence, this paper aims to present a field synchronization mechanism for DT of manufacturing systems for real-time data.

With the continuous growth of the urban population, the world is confronting numerous challenges in the near future of cities, related to environmental, economic and social aspects (e.g., traffic congestion, air pollution, waste management, etc.). The smart cyber city concept is introduced as a solution to achieve sustainability, effective management of energy, and smart living. Designing such complex systems with such features is an equally complex challenge. The cyber physical system (CPS) and the Internet of thing (IoT) are keys to achieve the realization of current smart city. In this paper, a survey of smart city vision is given while highlighting the potential benefits of a smart cyber city. A bibliometric network with Vosviewer software was created in order to select the most significant papers. It reviews smart city's key requirements and smart city enabling technologies. It then highlights the potential role of CPS and IoT. Finally, an outline of future research areas is given.

Digital twins (DT) constitute a major concept of future industrial systems. They are expected to enable efficient virtualization of manufacturing systems and enhance various decision-making processes. In parallel, many initiatives exhibited how artificial intelligence (AI) could increase the performance of the DT on specific applications. By reviewing the literature combining AI and DT, a lack of contributions on the whole life cycle of the DT was exhibited. Therefore, the main contribution of this paper is to define a global integration framework of AI into DT, focused on the exploitation phase of the DT. A case study, using a relatively simple physical twin, illustrates the potential of such integration for the response of the DT to unpredictable modifications of the physical twin.

Digital Twins (DT) have been used in a variety of industrial sectors and application areas in recent years. To avoid downtime and problems in manufacturing, DTs strive for real-time monitoring and optimization and anticipate the future status of the physical twin. Fault Detection and Diagnosis (FDD) is a maintenance field that has attracted the interest of the control and automation community as a viable means of ensuring the dependability and safety of industrial systems. An enhanced FDD methodology thought DT can provide significant benefits. The main contribution of this research focuses on the exploration of DT for FDD methodology and provides also a guideline of different alternatives in each step and highlights the importance of DT.