## 4 Results
### Feature Parameter Selection  
During the wind turbine operation, the SCADA system continuously collects and stores various operational status data, including operational data, fault alarms, maintenance records, and shutdowns. The operational data is typically multi-dimensional, complex, and nonlinear time-series data, encompassing numerous parameters such as ambient temperature, wind speed, gearbox oil temperature, and generator speed. When training a prediction model, an excessively high dimension of input variables can lead to a decrease in the model's generalization ability. To address this issue, this paper analyzes the wind turbine's operational characteristics, and selects 18 key feature parameters closely related to the gearbox condition from the SCADA monitoring parameters. The specific parameters are listed in Table 4.  
The gearbox is a critical component of the wind turbine, and its proper operation heavily relies on the normal circulation of lubricating oil. The oil sum temperature  
Figure 15: Wind speed–power curve after preprocessing. can reflect the internal mechanical performance and lubrication condition of the gearbox. Excessively high or low oil temperatures may indicate insufficient oil fluidity, which in turn affects the lubrication effectiveness, causing excessive wear or seizure and thus increasing the risk of failure. Therefore, this chapter selects the gearbox oil sump temperature as the monitoring point. Although all parameters listed in Table 4 may influence the gearbox oil sump temperature, the 18-dimensional input features still pose an issue of dimensional redundancy. To improve model training efficiency, the Pearson correlation coefficient method is used for feature selection. By quantifying the degree of correlation between each parameter and the oil sump temperature, the most relevant features are selected as model inputs. Figure 16 shows a heatmap of the strength of correlation between features. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Unit** & **Parameter Name** & **Unit** \\ \hline \hline \begin{tabular}{c} gearbox oil sump temperature \(x_{1}\) \\ \end{tabular} & \({}^{\circ}\)C & Main bearing (rear) temp. \(x_{10}\) & \({}^{\circ}\)C \\ \begin{tabular}{c} gearbox HSS (front) temp. \(x_{2}\) \\ \end{tabular} & \({}^{\circ}\)C & Avg. wind speed within 60 s \(x_{11}\) & m/s \\ \begin{tabular}{c} gearbox HSS (rear) temp. \(x_{3}\) \\ \end{tabular} & \({}^{\circ}\)C & Ambient temperature \(x_{12}\) & \({}^{\circ}\)C \\ \begin{tabular}{c} gearbox oil inlet temp. \(x_{4}\) \\ \end{tabular} & \({}^{\circ}\)C & Engine compartment temperature \(x_{13}\) & \({}^{\circ}\)C \\ \begin{tabular}{c} gearbox oil inlet pressure \(x_{5}\) \\ \end{tabular} & bar & Engine compartment humidity \(x_{14}\) & g/m\({}^{3}\) \\ \begin{tabular}{c} gearbox oil pump outlet pressure \(x_{6}\) \\ \end{tabular} & bar & Generator’s active power \(x_{15}\) & KW \\ \begin{tabular}{c} gearbox cooling water temp. \(x_{7}\) \\ \end{tabular} & \({}^{\circ}\)C & Generator’s reactive power \(x_{16}\) & KW \\ \
\begin{tabular}{c} footnot speed \(x_{8}\) \\ \end{tabular} & r/min & Generator speed \(x_{17}\) & r/min \\
\begin{tabular}{c} Main bearing (front) temp. \(x_{9}\) \\ \end{tabular} & \({}^{\circ}\)C & Engine compartment control cabinet temp. \(x_{18}\) & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters selected from SCADA data. Figure 16: Correlation coefficient matrix. In the feature selection process, parameters with an absolute Pearson correlation coefficient greater than 0.6 are selected as inputs for the model to ensure that the model can learn from more representative and informative features. This approach effectively reduces the interference of data noise, enhancing the model's generalization performance, and lowering the risk of overfitting. To effectively accelerate the convergence speed of the neural network, optimize the training process, and improve training stability, the input data is preprocessed by normalization.