## 1 Introduction
suggested multi-level planning and scheduling integrated with material restrictions in rolling horizon intervals, and they used the theory of constraints and the Drum Buffer Rope (DBR) mechanism for multi-level planning and scheduling in mixed-model production. They also proposed a multi-level planning heuristic (MLPH) using the DBR and priority rules for efficient planning and scheduling, taking into account material requirement planning, and they used the capacity constraint resource (CCR). The suggested multi-level planning heuristic outperforms the fundamental scheduling principles such as EDD, SPT, and LPT in the outcomes [20]. [PERSON] et al. [21] actually applied digital twin technology to production machine tools. Sensors collect data related to production machine tools in the physical space and realize the fusion of actual production machine tools and virtual machine tools information. In order to further expand the scope of the application of digital twin technology, the 2012 American National Standards and Technology Research Academy defines two major categories: MBD (Model-Based Definition) and MBE (Model-Based Enterprise). This concept was used in [22], thereby extending the scope of application of digital twin technology to the entire product life cycle, including pre-production product design simulation, mid-term product processing and production, and after-sales maintenance of later products. [PERSON] et al. [23] combined web services technology and augmented reality technology to simulate the virtual body in digital twin technology. The visualization was explored. Despite these challenges, the benefits of using a digital twin of a magnetic bearing system are clear. By providing real-time insights into the system's performance, the digital twin can help identify vibration for imbalance mass potential problems before they occur and optimize the control algorithms to improve performance and reduce energy consumption [24; 25]. Additionally, the digital twin can simulate different operating conditions and test different control strategies, providing valuable information for designing and developing new magnetic bearing systems [26]. In conclusion, the digital twin is a powerful tool for monitoring, analyzing, and optimizing the performance of magnetic bearing systems. It provides real-time insights into the system's performance and helps identify potential problems before they occur. Additionally, it can be used to optimize the control algorithms and test different operating conditions, providing valuable information for the design and development of new magnetic bearing systems. Despite the challenges, the benefits of using a magnetic-bearing digital twin are clear. It is expected to become a key technology in the field of industrial machinery and equipment. The objective of developing and modeling a digital twin for magnetic bearings is to create a virtual representation of the physical magnetic bearing system that can be used to optimize its performance, enable predictive maintenance, and diagnose faults. In this paper, the first section is an introduction, and the second section discusses the material and method used in modeling: the digital twin, magnetic bearing, and the digital twin modeling. The third section reports on the results obtained from the modeling.