# Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes
## 2 Related Works  
To carry out the state-of-the-art analysis, a review of the works undertaken in the industry in the last 5 years was carried out. This research reviews renowned databases (Elsevier, Science Direct, Web of Science, IEEEXplore, Springer, and Scopus) to find studies on DTs and mixed reality in the industrial field. The purpose was to explore various applications and architectures by analyzing proposals made by other authors and the enabling technologies for their development. The identification of articles began with extensive queries of academic literature databases for a series of thematically essential keywords. For example, the following keywords were used, typically in sets of two and groups of three or more: [Digital Twin, Augmented Reality], [Digital Twin, ISO 23247], [Digital Twin, Senso Glove], [Digital Twin, Immersive Environments], and [Digital Twin, Industry 4.0, Augmented Reality]. Furthermore, for the selection of the articles, it was sought that the methodologies proposed by the authors had been implemented at an industrial level and not only simulated, to guarantee the possible application. Works were sought that used robust industrial protocols and protocols that could be implemented in low-cost embedded systems. Regarding the programming language, the search was carried out in a general way because, in research, different languages can be used according to the level found according to the automation pyramid. Finally, DT papers implemented with mixed realities (virtual reality and augmented reality) were searched to learn about the characteristics and advantages they provide when combining these technologies. After the review of articles that met the characteristics, 15 articles were obtained, only 1 of which was based on the ISO 23247 standard, which shows that the proposals presented on the topic are quite open. One of the areas where DTs are used is in maintenance, where several investigations have been carried out. In [20; 21; 22], they present a DT to perform the predictive maintenance of a wind turbine and reliability in floating offshore wind turbines (FOWTs). The architecture presents a distributed DT with IIoT, machine learning (ML), MQTT, and fog computing to offer the better utilization and management of assets through real-time condition monitoring, predictive analysis, and the health management of selected wind turbine components in a wind farm. The architecture presents three layers: (1) the cloud layer, where there is deep learning and cloud computing; (2) the fog layer, where there are fog nodes for the DT interfaces, APIs, and SDK; and (3) the IoT device layer, where turbine sensors and actuators are located. The results show that the DT offers a good solution; however, it is an emerging technology that could be improved with better standards, architectural frameworks, and implementation methodologies. In turn, this shows that the integration of technology in FOWTs provides valuable information in the DT for reliability and performance optimization. Furthermore, it shows the potential of DTs as a valuable tool for renewable energy, boosting efficiency and sustainability in floating offshore wind installations. In [23; 24], they propose a DT-based method for fault diagnosis and prediction, aiming to compensate for the shortcomings of existing methods: the single fault type, low similarity, and poor visual effect of state monitoring. For this, a complete model is built that represents all the elements, processes, and businesses of the physical workshop using a testing platform to acquire real and simulated signals under various operating conditions. For prediction, they use the \"digital twin network + short-term memory (LSTM)\" algorithm. The results showed that the proposed method has a high rate of accuracy in the prediction for the production line because integrating the function of feedback allows for an accurate perception of the current state and future changes.