## 1 Introduction
The quality and reliability of consumables, including gear oil, is also an important factor in ensuring trouble-free gear operation, as oil contamination can affect lubrication efficiency and lead to failures [22]. Although some progress has been made in the dynamic modeling of planetary gear systems, the fact remains that the large number of factors affecting planetary gear systems leads to the need for the idealization of the model during the modeling process. In addition, there are deficiencies in the existing methods for the dynamic modeling of planetary gear systems, i.e., the influence of non-mechanical parts (e.g., drive motors) on the dynamic characteristics is neglected. Therefore, there is a need for a new and well-considered modeling approach. The digital twin is defined as an integrated multi-physical-field, multi-scale, probabilistic simulation process that builds a complete virtual mapping model of the equipment based on its available high-fidelity physical model, historical data, and real-time sensor update data to portray and reflect the full life-cycle process of the physical system and achieve the prediction of the equipment health status, remaining service life, and mission accessibility. Therefore, this paper adopts digital twin technology to build a planetary gearbox system model and consider the influence of non-mechanical factors on the model to more closely imitate the real situation and better guide the subsequent work on equipment health state assessment and remaining useful life (RUL) prediction. In terms of the digital twin, [23; 24] applied the digital twin to gears: the former validated the proposed digital twin model based on the provided numerical values and experimental examples; the latter discussed the use of the model in the prediction of the remaining service life of gearboxes. In the literature [25; 26; 27], a digital twin model was developed for wind turbine drive trains to achieve the remote monitoring of the wind turbine gear unit status and the prediction of downtime due to faults as well as for RULprediction. In the literature [28; 29], a digital twin fault diagnosis method was used to secure the operational safety of nuclear power plants. The works [30; 31] developed a digital twin predictive maintenance framework for air handling units (AHUs) and a digital twin model for fault diagnosis in distributed photovoltaic (PV) systems with rooftop building integration. In [32], a digital twin model for predicting impacts on helicopter composite sandwich structures was developed, which can estimate the damage before the inspection. The previous work [33] developed a digital twin model for detecting tool wear that can be used to identify the wear status of blades as well as to predict the development of wear based on future cutting schedules. Moreover, [34] proposes a comprehensive and robust digital twin architecture for power plants that achieve high reliability, availability, and maintainability. Another study [35] proposes a blockchain-based DT framework as a trusted digital twin model for securing cyber-physical systems (CPSs) in response to the complexity of cyber-attacks in CPSs. The works [36; 37] established a digital twin model for electric motors to achieve the fault diagnosis and optimization of electric motors. What is more, [38; 39] present a digital twin model for improving process and product quality and a digital twin framework that allows for the simultaneous modeling and estimation of multiple dimensions of system health. In summary, the digital twin technique was applied to level gears in [23], and the digital twin modeling in the [24] did not consider the modeling of the non-mechanical parts. The works [23; 24; 25; 26; 27; 28; 29] present a detailed description of the current application of digital twin technology, which provides theoretical guidance for the study of this paper.