# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 1 Introduction
In references [29; 30; 31; 32; 33; 34], the high-frequency resonance vibration signal of bearing is extracted by wavelet analysis. Combined with the classification ability of the support vector machine (SVM) and the dynamic time series processing ability of the hidden Markov model, a new bearing fault diagnosis scheme is proposed so as to improve the accuracy of bearing fault diagnosis. In reference [35], wavelet packet energy entropy is combined with empirical mode decomposition (EMD) to enhance the noise elimination of original vibration data and improve the accuracy of diagnosis. References [36; 37; 38] proposed a typical fault diagnosis method of the gearbox based on wavelet decomposition and support vector machine classification, a wind turbine bearing vibration fault diagnosis method based on noise suppression and a fault diagnosis method for the planetary gearbox of main drive chain of wind turbine under non-stationary conditions based on adaptive optimal kernel time-frequency analysis, respectively. References [39; 40; 41] show other methods of fault diagnosis based on wavelet transform. They all show that more effective typical fault features are extracted from the high-frequency vibration signals of the professional main drive chain vibration fault diagnosis system. Reference [42] began to introduce the influence of different working conditions on wind turbine fault analysis. Recently, more scholars used advanced algorithms such as neural network and machine learning for fault diagnosis in wind turbines [43; 44; 45; 46]. References [47; 48] compare the advantages and disadvantages of the neural network model and traditional condition monitoring analysis model in the fault diagnosis of wind turbines. Some other methods proposed fault diagnosis using different technologies such as thermal imaging [49]. They are non-invasive but limited to specific tested objects and show less universality than the commonly used SCADA and vibration fault diagnosis system. For instance, reference [50] uses thermal imaging to evaluate the condition of only angle grinders in wind turbines. Moreover, wind turbines usually operate in wild environments with variable temperatures, and, therefore, evaluating the wind turbine using only thermal imaging is possible to be disturbed in operating states. Reference [51] uses ultrasonic reflectometry for detecting while the targeted fault to be detected is limited to the lubrication failure of the wind turbine bearing. The main drive chain vibration fault diagnosis system and wind turbine SCADA system are two of the most popular diagnosis systems for wind turbines. However, their suppliers are independent of each other so the data of these two systems cannot be shared, and, therefore, each of the research projects above is based on one of these two data sources for fault diagnosis. In this paper, a data interface between the two systems is established through the technical transformation of the two systems by the wind farm owners. An approach of using a data fusion method of two types of data to extract fault features of the wind turbine main drive chain is proposed and paves a new way to improve the accuracy of fault diagnosis of the main drive chain in the wind turbine. The contributions of this study are as follows:  
1. Proposing a fault diagnosis strategy of the main drive chain in wind turbines based on data fusion, considering both the real-time monitoring data from the SCADA system and the high-frequency vibration data of the main chain. 2. Proposing the detailed method to classify and extract the fault features based on two types of data and the method for fault diagnosis using the deep autoencoder model. 3. Conduct case studies in a real wind farm to verify the effectiveness of the proposed strategy, and analyze the experimental results and the benefits to the high-efficient operation and maintenance of wind turbines.