## 1 Introduction  
Magnetic bearings are a relatively new technology that has been gaining popularity recently, particularly in industrial machinery and equipment [1]. Unlike conventional bearings that rely on physical contact between the rotor and the stator, magnetic bearings use electromagnetic forces to levitate and stabilize the rotor [2]. This allows for several advantages over standard bearings, such as increased efficiency, excellent reliability, and the ability to operate in harsh environments [2]. However, these benefits come at the cost of increased complexity and the need for precise control and monitoring of magnetic forces [3]. A digital twin is a virtual representation of a physical asset or system that monitors, analyzes, and optimizes performance [4]. A digital twin may be utilized to simulate the behavior of a magnetic bearing system in real time, giving important insights into the system's performance and detecting possible problems before they develop. The control algorithms used to steady the rotor may also be enhanced with the help of the digital twin, leading to better performance and less energy usage. Building a digital twin of a magnetic bearing system requires accurate and detailed models of the system and real-time sensor data from the physical system [5, 6]. The digital twin must be able to accurately simulate the physical behavior of the system, including the electromagnetic forces that are used to levitate the rotor [7]. Additionally, the digital twin must be able to incorporate real-time sensor data from the physical system, such as the position and speed of the rotor, to provide accurate and up-to-date information on the system's performance [8]. One of the key challenges in building a digital twin of a magnetic bearing system is the need for precise control and monitoring of the magnetic forces [9]. This requireshigh-precision sensors and control algorithms that can accurately measure and control the magnetic forces, even in the presence of noise and other disturbances [10]. Additionally, the digital twin must be able to handle the complex dynamics of the system, including the interactions between the rotor and the stator and the effects of external disturbances such as vibrations and temperature changes [11; 12]. [PERSON] et al. [13] realized the accurate detection of equipment failure by establishing an accurate digital model of equipment failure. [PERSON] et al. [14] modeled the equipment loss process and the normal operation state of the equipment to realize a condition assessment and equipment failure prediction. [PERSON] et al. [15] used neural network technology and wavelet transform technology. The technique was used to analyze the equipment operation data, and the method's feasibility was verified by predicting the failure of rotating machinery. [PERSON] et al. [16] discussed the fault diagnosis of wind turbines and proposed a method, based on the nonlinear model of the fuzzy system and neural network, to predict equipment failure and used this method to predict the wind. The failure of the power turbine was tested. [PERSON] et al. [17] used digital twin technology to achieve rapid design and optimization of glass production according to production needs, based on the actual glass combined with the glass production line. [PERSON] et al. studied how project management is shifting toward smart manufacturing in the present and the future; as a consequence, companies that are smart can take advantage of enhanced flexibility and quick lead times, as well as improved maintenance, product quality, resource usage, and efficiency monitoring [18]. To make wise planning choices to deal with the rising demand and profit-enhancing methods, [PERSON] et al. integrate information sharing from planning issues, printed circuit board (PCB) assembly lines, retailers, and shipping departments with a cloud manufacturing system (CMS) [19]. [PERSON] et al.