# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
## 1 Introduction  
In the face of global energy shortages and increasing atmospheric warming, wind power generation, producing clean and renewable energy, has become one of the most trusted forms of power generation [1]. However, wind turbines often break down due to their harsh operating environment, leading to unexpected shutdowns and economic losses [2]. The wind turbine transmission system gear is often complicated in structure, has a high failure rate, and failure sites are not easy to find in time. The accompanying economic loss accounts for the largest proportion of each fan component [3]. Therefore, realizing the real-time monitoring and intelligent diagnosis of the planetary gear of wind turbines is of great significance for safe and stable operation. At present, gear fault diagnosis methods mainly include those based on expert systems, physical models, and those that are data-driven [4; 5; 6]. Compared with the first two methods, the data-driven fault diagnosis method has the advantages of low requirements for professional knowledge and easy access to data. In this era of mature sensor technology [7] and big data analysis, scholars in the industry favor the data-driven fault diagnosis method. Data-driven fault diagnosis methods mainly include traditional machine learning algorithms [8] and deep learning models [9]. Although deep learning models, such as convolutional neural networks [10], long- and short-term memory networks [11] and automatic encoders [12], have outstanding achievements in feature extraction, they still have defects such as large sample sizes and long training times. Traditional machine learning algorithms can perform better when historical data samples are limited. However, all the data-driven diagnosis methods collect the data, conduct an offline analysis, and then obtain the diagnosis results. Therefore, it is crucial to seek new technical methods to solve the problems of the data acquisition, condition monitoring, and fault diagnosis of planetary gear of wind turbines during operation. With the development of the fourth Industrial Revolution, the need for digital and intelligent equipment operation and maintenance is becoming increasingly urgent. Researching digital twin technology and machine learning algorithms that combine data and physics has become a top priority. Professor [PERSON] first proposed the digital twin concept in 2002 [13], which establishes a mirror model of a physical entity in the digital space. With the continuous innovation of network information technology, digital twin technology has also been rapidly developed. The virtual model and physical entity data connection interactive mapping are completed by establishing the digital twin model and using sensor technology. Furthermore, the simulation and health monitoring, diagnosis, and maintenance can be realized by analyzing the collected data information [14]. As the research of digital twin technology becomes more in-depth, the recurrence of technology is becoming increasingly mature; the development potential is huge, and the technical concept is constantly expanding. At the same time, digital twin technology is becoming more widely used in online monitoring and intelligent equipment diagnosis. For example, [PERSON] et al. [15] proposed a five-dimensional digital twin model, which added the composition of the service system and communication connection to the original three-dimensional structure and realized the fault prediction and health management method driven by the digital twin. However, this research is only theoretical, and much work is needed before a complete reproduction can be achieved. [PERSON] et al. [16] proposed a ship structure bearing a monitoring system based on a digital twin. Through a local stress correlation method, the stress monitoring data collected by sensors are applied to the digital twin model of the ship monitoring structure to realize the health management of the ship structure and greatly reduce the cost of ship maintenance.