# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 5 Conclusions  
This article demonstrates the shortcomings of the commonly used main drive chain fault diagnosis methods that only use a single data source. Then a method of fault features \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Turbine**} & \multirow{2}{*}{**Fault Type**} & \multirow{2}{*}{**Fault Location**} & \multirow{2}{*}{**Abnormal Phenomena**} & \multirow{2}{*}{**Maintenance after Early Warning**} & \multirow{2}{*}{**Duration between Early Warning and Alarm from SCADA**} \\  & & & & & \\ \hline \#23 & Broken tooth & \begin{tabular}{c} Gear at medium \\ speed shaft \\ Minor gear at \\ medium speed \\ shaft \\ \end{tabular} & \begin{tabular}{c} Sideband signal in \\ frequency spectrum \\ Sideband signal in \\ frequency spectrum \\ \end{tabular} & Yes & - \\ \#32 & Broken tooth & \begin{tabular}{c} Gear at medium \\ speed \\ medium speed \\ shaft \\ \end{tabular} & \begin{tabular}{c} Sideband signal in \\ frequency spectrum \\ \end{tabular} & Yes & - \\ \#15 & Broken tooth & \begin{tabular}{c} Gear at medium \\ speed shaft \\ \end{tabular} & \begin{tabular}{c} Sideband signal in \\ frequency spectrum \\ Abnormal peak in \\ frequency spectrum near \\ the characteristic \\ frequency of \\ planet bearing \\ \end{tabular} & No & 14 days \\ \#13 & Corrosion & \
\begin{tabular}{c} Outer raceway of \\ rear bearing \\ \end{tabular} &
\begin{tabular}{c} Exorbitant peak value of \\ vibration signal \\ \end{tabular} & No & 5 days \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of diagnoses of to typical faults in wind turbine main drive chain. Figure 14: Gearbox med-speed gear broken tooth fault in wind turbine A.  
extraction and fault diagnosis based on data source fusion is proposed. The new method makes integrated uses of the globality of the wind turbine SCADA system and the pertinence and depth of the vibration fault diagnosis system for main drive chain in wind turbines to solve the problem that background noises from a single data source are difficult to process. In the proposed method, fault features of the main drive chain are jointly extracted and a deep self-encoding network fault diagnosis model based on data fusion is established by integrating SCADA real-time monitoring system data with main drive chain vibration monitoring data. The parameters obtained by the unsupervised learning training of the deep auto-encoding network can be used as the prior information of the following supervised learning model. Using labeled data sets for supervised learning further optimizes the deep auto-encoding network model and improves the accuracy of fault diagnosis. The experimental results show that the diagnosis system using the proposed method accurately located the gearbox broken tooth fault in a wind turbine at a very early phase before the traditional SCADA system raised any alarm. That diagnosis avoided further expansion of this failure followed by greater loss. Obviously, this new approach provides strong technical support for the operation and maintenance of wind turbines with more immediacy and efficiency. There is a possibility to use the way of fusing data from multiple sources for other problems. However, the scenes and methods proposed in this article are specific and highly concentrated. More complete and pertinent analysis and experiments must be done for another specific problem. Future related research will be focused on classification and recognition of possible original causes of the detected faults. Various types of faults and the original reasons will be analyzed. It will allow for the pre-analysis and early warning of faults before the manual detection and will improve the efficiency of the operation and maintenance of wind turbines. Conceptualization, Z.X. and P.Y. ; methodology, Z.X. and P.Y. ; software, Z.X. ; validation, Z.X., P.Y. and X.W. ; formal analysis, Z.X. ; investigation, Z.X. and C.S.L. ; resources, P.Y, Z.Z. and X.W. ; data curation, Z.Z. and X.W. ; writing--original draft preparation, Z.X. and X.W.