# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 6 Discussion  
A data-driven approach was selected for converter fault detection using SCADA system data in this paper. It was found that the proposed CNN network, AOC-ResNet50 network model, is better than other competitive CNN network models, such as ResNet50 and Oct-ResNet50 network models, in converter fault detection using the SCADA system data. This is due to the advantage of the AOC-ResNet50 network that it avoids the information asymmetry and the damage to the original features in the high and low frequency domains in the information sampling by comparing to the OctConv structure.  
Although the AOC-ResNet50 network can provide higher accuracy in fault detection, it requires a large data sample for training the model, and the detection accuracy also relies on the data quality. Another disadvantage is that the algorithm associated with the AOC-ResNet50 network is more complex and requires the developer to have a strong mathematical background to understand the network structure and very good skills in software programming.  
Selection of an approach to fault detection or diagnosis depends on the available signals and data recordings. If there are direct measurement signals corresponding to a failure mode, the methods based on signal processing and analysis would be preferred. If there are no direct measurement signals but there are indirect measurement data and operation data, a data-driven approach would be selected. In this case, CNN and other neural network models can be selected for testing. In general, if it is a complex system with multiple-dimension data available including operation and condition monitoring data, the newly proposed AOC-ResNet50 network as well as other typical CNN models are recommended for trial.  
In the cases where there are direct measurement signals and the data sample size is large enough, the AOC-ResNet50 network and other typical CNN models are also recommended for application. In this situation, one may carry out a comparative study of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **Specificity** & **Negative Precision** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline AOC–ResNet50 & 96.11 & 93.57 & 99.03 & 93.19 & 98.97 \\ Oct–ResNet50 & 91.57 & 86.61 & 98.35 & 84.78 & 98.09 \\ ResNet50 & 89.25 & 85.28 & 94.88 & 83.63 & 94.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Converter fault detection performance indices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Days before** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **Negative Precision** \\
**failure** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
3 days & 98.04 & 98.41 & 97.66 & 98.43 & 97.68 \\
7 days & 96.11 & 93.57 & 99.03 & 93.19 & 98.97 \\
15 days & 92.51 & 92.99 & 91.92 & 93.07 & 92.01 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Converter fault detection performance indices by AOC–ResNet50.  
the model performance by making a comparison between the CNN models and the models developed using signal processing and analysis methods, e.g., to detect and diagnose bearing faults for failure prediction [61].