Neural Network Modeling as a Method for Creating Digital Twins: From Industry 4.0 to Industry 4.1

Digital twins are one of the key technologies behind the Fourth Industrial Revolution. In the coming years they will be introduced on a large scale in the industry and in other spheres. A wide range of digital twins will be in demand: from separate components to complex technical facilities, such as automobiles, airplanes, manufacturing lines, factories, corporations, etc. To provide their successful interaction, it is important to create digital twins on the uniform principles. Currently, creating a digital twin is a complex scientific issue. It presents difficulties because it is necessary not only to describe physical (or chemical, biological, etc.) processes going on in the object, but also to envisage significant changes of its properties in the course of its operation. In this case the digital twin is supposed to adapt to the changes in the original object in accordance with the data received from the sensors. The aim of the research was to define the strategies of solving the current problems in such areas as digital twins, the internet of things and cyberphysical systems. In order to achieve this aim, the following problems were supposed to be solved: - Consider the definitions of the digital twin suggested in the world scientific literature - Find a unified data-driven real-time approach to creating digital twins - Suggest using the neural network approach in creating digital twins. During the use of the modelled object, specifics of the physical processes going on in it and object properties can change. The model is supposed to adapt in accordance with these changes, which is rather difficult if a model is generated by applying computer-aided engineering software packages (CAE) based on classical numerical methods. We consider the multistage technique as more promising. It involves building an adaptive model at the second stage. Such a model can be specified and redesigned based on real-time data. Since neural networks have proved to be efficient in solving complicated problems related to data processing, we recommend using them as the basic class of mathematical models for creating digital twins.