# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## 1 Introduction
The research and application of digital twin in the industrial field are summarized, and the problems existing in the theory and practice of digital twin are deeply analyzed. Based on the digital twin technology combined with machine learning methods, a wind turbine digital twin fault diagnosis system was built, realizing the practical application of digital twin technology. This fully embodies the characteristics of the digital twin in real-time, but its research at the level of model mapping is still lacking. In summary, the research of digital twin technology in various fields is being gradually developed, and the characteristics of digital twin technology with high efficiency and high visualization degree are gradually revealed. Therefore, it is necessary to use digital twin technology to study the fault diagnosis of wind turbine drive systems to solve the problem of low fault diagnosis efficiency and low visualization degree of wind turbines. At present, in the realization of large-scale wind turbine equipment lean management, the safety and stability of wind turbines still faces many challenges, which can be mainly summarized as the following two points: (1) the status quo of the increase in installed capacity of wind turbines and the negative growth of equipment operation and maintenance personnel, which in turn leads to the contradiction of the futile increase in its fault recurrence, is becoming increasingly prominent [9]; (2) the traditional state evaluation methods in the wind turbine operation state data mining is not sufficient, and it has the defects of insufficient ability to discover hidden dangers and low degree of visualization [10]. In view of the above problems and existing research, it is necessary to construct a wind turbine drive train fault system based on digital twin technology and integrate machine learning methods to realize the functions of multi-dimensional and multi-scale real-time monitoring of wind turbines and trend prediction and analysis of equipment deterioration status. Fault data, as the main driving force of industrial equipment fault diagnosis and intelligent operation and maintenance, can be used to analyze the collected historical operating state information of wind turbines through data machine learning technology, identify their operating state categories, and carry out fault early warning, localization, and root cause analysis in a timely manner. Many researchers and scholars, at home and abroad, have focused on basic data-driven approaches to diagnosing equipment operation state information. In the field of agriculture, [PERSON] et al. [11] proposed a corn harvester fault diagnosis method based on ABC-VMD and optimized Efficient-Net. This method achieved an overall classification accuracy rate of 98%, successfully realizing fault state monitoring for corn harvesters. [PERSON] et al. [12] proposed a new intelligent fault diagnosis method based on a convolutional neural network (CNN) for rotating machinery with few samples, with an average diagnosis accuracy of 93.38% and good diagnosis results. [PERSON] et al. [13] proposed a multiple-constraint modal-invariant graph convolutional fusion network (MCMI-GCFN)-based bearing fault diagnosis method with 99.6% diagnosis accuracy for bearings, which can effectively realize bearing fault diagnosis. The above method proves that data mining technology provides a reliable solution to solve the wind turbine drive train fault information detection and realize the rapid fault location and root cause analysis. But it could perform better in terms of visualization. Combining digital twin technology with data mining, on the one hand, with the help of the digital twin's powerful three-dimensional immersive visual expression, can improve the degree of visualization of fault state results. On the other hand, data mining means mining the fault characteristics hidden in the massive operation and maintenance status information and then realizing the rapid and accurate diagnosis of the wind turbine drive train system and fault root cause analysis.