# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 5 Conclusions  
We aimed to solve the problem of the gearbox of wind turbine generators bearing variable and heavy loads in working conditions and the dataset being small. It is difficult to obtain a high recognition rate for gear faults; so, we proposed a fault diagnosis method for wind turbine gearboxes based on Mel spectrograms and a CBAM-ResNeXt50-ArcLoss transfer learning model. The original sound signal is processed by Mel spectrograms to generate a two-dimensional feature map containing both time and frequency domains. The dimension of the frequency-domain signal is reduced from frequency to logarithmic Mel scale, which reduces the redundant information of the features, reduces the interference of noise, and improves the processing efficiency of the signal. In the sound signal classification task containing the operating state information of the gearbox, the generated Mel spectrogram is input to CBAM-ResNeXt50-ArcLoss for training and verification, and a high accuracy rate of fault classification is achieved. The CBAM enables the network to adaptively adjust the degree of attention to different feature channels and spatial locations, and ArcLoss reduces the intra-class differences, increases the inter-class differences, and enhances feature discrimination. The ResNeXt structure improves the accuracy without increasing the complexity of the parameters, and the transfer learning method using fine-tuning gives the model better generalization. Compared with the CNN, ResNet50, ResNeXt50, and CBAM-ResNet50 methods, the CBAM-ResNeXt50-ArcLoss model shows improvements of 13.3, 3.6, 2.4, and 1.3, respectively. This method provides a new idea for the fault diagnosis of wind turbine gearboxes and has potential engineering value for fault diagnosis, operation, and maintenance.  
It should be noted that the proposed method was validated on a balanced dataset. In practical applications, the number of healthy-state samples far exceeds that of fault samples, and this class imbalance issue may reduce diagnostic sensitivity. Although the ArcLoss loss function and CBAM themselves can enhance the ability to distinguish features of minority classes, further improvement measures are still recommended for scenarios with severe imbalance. Future research will focus on verifying the robustness of the model under imbalanced data distributions in the real world.  
In this study, controlled experimental data from a single wind turbine gearbox were utilized. Known faults were artificially implanted, and audio signals were collected under laboratory operating conditions. This ensures the reliability of fault labels and the effectiveness of the initial method. However, a limitation of this study is that the training and validation data are derived from a single gearbox, which may not fully cover the complexity of real-life scenarios (such as operating environments with extreme weather, differences in gearboxes under various scenarios, etc.). Future research will validate the abovementioned situations.  
X.Z.: Methodology, Formal analysis, Software, Validation, and Writing--original draft. F.J.: Software and Validation. Y.C.: Funding acquisition, Methodology, Project administration, and Writing review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was founded by Science Research Project of Hebei Education Department (CXY2024016).  
\begin{table}
\begin{tabular}{c c c} \hline
**Loss Function** & **Accuracy** & **Loss Value** \\ \hline Softmax & 98.5 & 0.113 \\ Triplet Loss & 99.1 & 0.102 \\ ArcLoss & 99.8 & 0.054 \\ \hline \end{tabular}
\end{table}
Table 4: The accuracy and loss value of the CBAM-ResNeXt50 model using different loss functions.