Artificial Data Generation in the Implementation of Digital Twins: Techniques and Applications
DOI:
https://doi.org/10.24425/mper.2025.157218Abstract
The growing dependence on high quality data in industrial environments drives the adoption of artificial data generation techniques, especially in the development and implementation of Digital Twins (DTs). This article presents a critical review of the main approaches to creating synthetic data, with an emphasis on their application in Industry 4.0 intelligent cyber-physical systems. Initially, traditional techniques such as Random Oversampling (ROS), SMOTE and its variants are analyzed, as well as statistical models such as the Gaussian Mixture Model (GMM). Next, Deep Learning-based methods are explored, namely Autoencoders, Variational Autoencoders and Generative Adversarial Networks (GANs), highlighting their ability to produce realistic and diverse data. The study also includes the analysis of practical cases in which DTs have been developed using synthetic data, covering domains such as wind energy, aviation and urban infrastructure. In this way, the aim of this study is to critically explore the different techniques of artificial data generation with an integration with the technology of DTs. The results suggest that the appropriate use of synthetic data can not only overcome limitations related to privacy and the scarcity of real data, but also improve the robustness and effectiveness of Digital Twins models. The article concludes by discussing the current challenges and future opportunities in integrating these techniques into smart industrial environments.References
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