# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## Case Study
### Implementation of Fault Diagnosis System of Wind Turbine Transmission System Based on Digital Twin  
In this paper, a wind farm with a 750 KW fan is taken as an example. The digital twin-based wind turbine drive train fault diagnosis system proposed above is tested for normal and rolling body failures. The sensor selection includes through-frequency acceleration sensors, and their performance indicators are presented in Table 5; the position of each sensor is shown in Figure 19. Data acquisition is performed using a microprocessor-based intelligent data acquisition system, which comprises several distributed boards and other components. Eventually, various sensors' outputs are converted into signals that can be accepted by the A/D converter. The digital twin fault diagnosis system established in this paper is shown in Figure 20, which is divided into four modules: the main interface, the status monitoring module, the digital twin model module, the fault diagnosis module, and the alarm recording module. The left side of the main interface shows the basic information about the wind turbine, and the middle screen shows the digital twin model monitor, in which the monitoring position is the wind turbine drive train, and the monitor can be free to slide the viewpoint through the monitor to observe the operation status of the wind turbine directly. The bottom right is the real-time vibration information when the wind turbine is running in the time-domain diagram, and the bottom left is the history of the running information. The condition monitoring module is capable of responding to collected data presented in both the time domain graph and the frequency domain graph. This study encompasses a total of six data monitoring channels, each corresponding to one of the six sensors integrated into the system. Users can freely switch between the data from any of these sensors, allowing for comprehensive monitoring and analysis. Each sensor provides real-time data that can be visualized in the time domain graph, which shows how the sensor readings change over time, and the frequency domain graph, which displays the  
Figure 19: Location of sensors in wind turbines. \begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor Type** & **Sensitivity** & **Measuring Range** & **Frequency Range** & **Operating Temperature** \\ \hline RH103 & 100 mV/g & 80 g & 0.5–15,000 Hz (\(\pm\)3 dB) & \(-\)40–120 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table} 
Table 5: Through-frequency acceleration sensor parameters. distribution of the sensor signal frequencies. This dual-domain analysis enables a more detailed understanding of the equipment's operational status and potential anomalies. The interface is shown in Figure 21. By taking sensor 2 example, the interface reads the collected data and performs a Fourier transform on the data to display the time domain diagram of the data. Figure 21: Vibration monitoring interface of digital twin system of wind turbine drive system. Figure 20: Wind turbine transmission system fault diagnosis system main interface. The digital twin modeling module is used to respond to the actual operating conditions of the physical equipment. The model is driven by a built-in kinematic model that can simulate the actual operation of the physical equipment based on information such as wind speed, spindle speed, blade angle and fault type. During the operation of the equipment, the digital twin model can collect and process various sensor data in real time, so as to accurately reproduce the operation status of the physical equipment. When a fault is encountered, the model can directly locate the screen to the fault location and can freely slide the viewpoint to observe the condition of each wind turbine component, helping staff to quickly find the fault and analyze the cause. The digital twin model not only reflects the operational status of the equipment in real time, but also detects possible faults and issues timely alerts, thus improving the maintenance efficiency and operational reliability of the equipment.