# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 1 Introduction
Digital twin technology integrates intelligent sensing technology, co-simulation technology, data analysis technology, and intelligent control technology, which can effectively synchronize virtual and physical products by making full use of data such as physical models, sensor updates, and operation history [27; 28]. It has three components: a physical entity, a virtual model corresponding to the physical entity, and the data interaction between the two [29]. Digital twin technology is able to build a bridge between virtual models of physical objects. The virtual models are used to describe the characteristics, performance, behaviors, and rules of physical entities. The dynamic behavior, actual status, and performance of physical objects are then monitored and predicted. Finally, the reverse control of physical objects is realized. By establishing a twin model of a physical entity and using sensor monitoring, data transmission, and other technologies, digital twin achieves real-time interactive mapping with the physical entity in the digital space, reflecting the full life cycle of the physical entity [30]. Although the development of digital twin is recent, it has shown a great application potential. In recent years, digital twin technology has been highly studied by many scholars, and the digital twin model has evolved from the original three-dimensional structure to a five-dimensional structural model, consisting of the physical entity, virtual model (co-simulation models), twin data, service system, and communication connections [31; 32; 33]. Meanwhile, the applications in equipment monitoring and prediction [34; 35; 36; 37] of digital twin technologies are also increasingly extensive. For example, [PERSON] et al. [38] applied digital twin technology to research equipment fault prediction and health management, proposed a five-dimensional digital twin-driven fault prognostics and health management method, presented the framework and workflow of the digital twin-driven prognostics and health management, and proved the effectiveness of the proposed five-dimensional digital twin method through a case study of gearbox prognosis. [PERSON] et al. [39] proposed a prognostics and health management system based on advanced digital twin technology. Through the finite element analysis of the digital twin model, the cable-net current safety status of the five-hundred-meter aperture spherical radio telescope (FAST) is evaluated, and the fatigue life of components in the cable-net is predicted. The prognostics and health management system has achieved good results during the commissioning of the FAST, greatly improving the maintenance efficiency and reducing its maintenance cost. [PERSON] et al. [40] developed a health monitoring and prognosis of electric vehicles (EV) permanent magnet synchronous motor by creating intelligent digital twin (i-DT) in MATLAB/Simulink to realize the performance monitoring of the motor both in-house and remotely. Finally, the simulation results indicated that the developed i-DT twins the present health of the EV motor remotely and in-house and can be implemented in EVs and autonomous vehicles. Meanwhile, digital twin technology also provides a new idea and methodology for researching the gear performance analysis and manufacturing. For instance, in terms of the gear performance prediction and evaluation, [PERSON] et al. [41] reviewed digital twin-driven remaining useful life prediction methods for the gear performance degradation, from the view of a digital twin-driven physical model-based and virtual model-based prediction method. By evaluating the advantages and disadvantages of various methods, it was proved that digital twin technology is suitable for gear performance degradation assessment and remaining useful life prediction. In order to solve the dynamics characteristics problems of the gear transmission system, [PERSON] et al. [42] established a digital twin model of gear transmission stability based on the three aspects of physical prototyping, twin service system, and virtual prototyping.