# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 1 Introduction  
The smart city concept is an advanced trend for the development for cities today and some crucial technologies such as Internet of Things (IoT), renewable energy, and smart grids are integrated to build the intelligent energy system in a smart city [1, 2, 3, 4, 5]. To increase the share of renewable energy in electricity generation and avoid the challenges caused by the centralized construction, centralized grid connection, and long-distance transmission of large-scale wind farms far away from the load center, distributed wind turbines are being widely used in development of smart cities [6, 7, 8]. The operation and maintenance of wind turbines distributed around the whole smart city are more difficult than the operation and maintenance of wind turbines in a centralized large-scale wind farm. In the wind turbines, electrical components have the highest fault frequency, followed by the main drive chain components. However, the electrical component faults can be located quickly, and the time to recover is short. Compared with the electrical component, the main drive chain component faults have a longer positioning time. Because of their huge size and heavy weight, it is cumbersome to replace them, and they need more time to recover. The outage of wind turbines before the replacement of the main drive chain fault components severely affects the operational reliability. So the wind turbines are in urgent need of economic efficiency of the wind turbines and the accurate and highly efficient remoteintelligent operation and maintenance service. Therefore, it is imperative to establish the fault diagnosis system with high precision for the wind turbine main drive chain [9; 10; 11]. To improve the accuracy of main drive chain fault diagnosis in wind turbines, scholars worldwide have carried out a lot of research on the fault diagnosis of main drive chain. Among all the researchers, most of them try to alarm the out-of-limit main drive chain based on the real-time monitoring data of the wind farm supervisory control and data acquisition (SCADA) system [12; 13; 14; 15; 16; 17] so as to maintain the normal operation state of wind turbines. Based on the real-time monitoring data of the SCADA system and the out-of-limit alarms of the main drive chain, the fault diagnosis framework of the wind turbine is established, and the out-of-limit diagnosis indexes of typical faults are given [18]. The research uses high-frequency SCADA data to extract the core technical indicators to improve the performance of wind turbines [19]. Further, the real-time monitoring data of the SCADA system is used to establish the fault prediction model of wind turbines [20]. More approached-based data analysis of the SCADA are being used today [21; 22]. For instance, a data-driven method is proposed to diagnose the pitch fault of wind turbines [23]. Reference [24] authors also use a data-based prognostic system without any additional sensor out of the SCADA. This paper [25] summarizes various types of real-time monitoring systems of wind turbines and states the advantages and disadvantages of various methods for fault monitoring of wind turbines based on the real-time monitoring data of the SCADA system. To improve the accuracy of the main drive chain fault diagnosis in wind turbines, people began to use the professional fault main drive chain diagnosis system for high-frequency vibration data acquisition and fault analysis to its main components [26]. Damage can be detected based on the differences between modified modal displacements in the undamaged and damaged states [27]. Based on the high-frequency vibration signal analysis of the main drive chain fault diagnosis system in reference [28], the gearbox faults under the non-stationary state of speed and load were statistically analyzed. Compared with the out-of-limit alarm signal in the low frequency real-time monitoring system of the SCADA system, the high-frequency signal analysis can locate the gearbox faults more accurately.