# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
## 1 Introduction
However, this study only focuses on the analysis and research of offline data collection and needs the capability of real-time monitoring and diagnosis. [PERSON] et al. [17] proposed a gear test bench condition monitoring method based on a digital twin. By obtaining the physical information, the digital twin model simulates the gear running state in real time and carries out simulation analysis and verification to realize the monitoring of the running state and improve the operating stability of the gear test bench. However, this method only realizes condition monitoring based on real-time data and does not continue to complete further data mining analysis. Aiming at the problems of inconvenient multi-robot monitoring and poor real-time performance, [PERSON] et al. [18] developed a set of multi-robot monitoring systems based on digital twin technology. By collecting data through OPC Unified Architecture (OPC UA), they realized the multi-robot cooperative collision test and improved the factory's production efficiency. However, this system only involves monitoring the robot arm's working state. It does not involve monitoring the equipment's operating state and deeper data analysis. Digital twin technology also provides a new concept and method for intelligent equipment fault diagnosis. [PERSON] et al. [19], aiming at the problem of obtaining fault data in advance for rolling-bearing fault diagnosis, proposed a rolling-bearing fault diagnosis method based on a digital twin. By constructing a dynamic virtual model to generate simulation data and adopting a partial adaptive algorithm, the twin model can accurately diagnose the health condition of actual rolling bearings under unknown fault conditions. Thus, the safe operation of bearings is ensured, and the maintenance cost is reduced. However, this method is still in the offline data analysis and diagnosis stage and cannot achieve real-time online analysis and diagnosis. [PERSON] et al. [20] developed a real-time field-programmable gate array-digital twin (FPGA-DT) technique. By constructing recognizable feature vectors and combining digital twinning technology to diagnose and classify system faults, the online real-time diagnosis of the transformer is realized, greatly improving the efficiency and accuracy of fault diagnosis. However, the system hardware requirements are relatively high and cannot universally achieve the system. [PERSON] et al. [21] proposed an improved random forest (IRF) algorithm based on a digital twin. The digital twin's fault diagnosis method added to the basis of the traditional machine learning algorithm not only improves the fault diagnosis accuracy but can also quickly discover and locate the fault location. Focusing on the disadvantage that historical and static data drive traditional satellite system fault diagnosis and health monitoring methods, [PERSON] et al. [22] proposed a fault diagnosis method for complex satellite systems based on a digital twin. By integrating simulation data and real-time data, the real-time diagnosis and maintenance of in-orbit satellites are realized, and the reliable operation of the satellite system is ensured, greatly reducing the operator's workload. However, the requirements and accuracy of the data are very strict, so the diagnosis cannot be realized quickly or conveniently. The above work shows that digital twin technology has good application prospects and excellent practicability in equipment condition monitoring and fault diagnosis. However, there are few studies on the condition monitoring and intelligent maintenance of planetary gear of wind turbines by using digital twin technology and online fault diagnosis by collecting real-time data. Given this, a fault diagnosis system for planetary gear of wind turbines based on digital twinning is proposed in this paper. The strain signal of the gear is collected and analyzed for online diagnosis, and it is successfully applied to the fault diagnosis test bench of the wind turbine.