# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 4 Discussion
### Experiment of Speed  
In this part, the length of the layer was 20 and the epoch was 3000. The testing time was tested on different devices: a desktop computer and the Raspberry Pi. The results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples of Segment** & **Devices** & **Accuracy** & **Testing Time** & **Sampling Time** \\ \hline \multirow{2}{*}{32} & Desktop & 99.8\% & 0.009 ms & \multirow{2}{*}{2.5 ms} \\  & Raspberry Pi & 99.8\% & 0.059 ms & \\ \hline \multirow{2}{*}{64} & Desktop & 99.7\% & 0.005 ms & \multirow{2}{*}{1.25 ms} \\  & Raspberry Pi & 99.7\% & 0.028 ms & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results and time of raw data on Raspberry Pi and desktop computer.  
Figure 10: Accuracy under different numbers of nodes in the fully connected layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples in Each Segment** & **Fault Type** & **Training Segments** & **Testing Segments** \\ \hline \multirow{2}{*}{32} & Normal & 19,200 & 4800 \\  & Fault & 19,200 & 4800 \\ \hline \multirow{2}{*}{64} & Normal & 9600 & 2400 \\  & Fault & 9600 & 2400 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of different segments.  
On the Raspberry Pi, for 64 samples of segments, the testing data were divided into 2400 segments, and each segment had 64 points.  
The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.059 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their occurrence.  
Regarding the 32 samples per segment, the testing dataset was divided into 4800 individual segments, with each segment consisting of 32 samples. In terms of system performance, the proposed approach exhibits efficient data processing capabilities, enabling swift analysis and prediction within a remarkable timeframe of 0.028 milliseconds per data segment. The experimental findings underscore the model's exceptional accuracy, with a reported 99.7% detection rate for wind turbine bearing faults within milliseconds of their manifestation.  
On the desktop, for 64 samples of segments, the testing data were divided into 2400 segments, and each segment had 64 samples.  
The proposed system can efficiently process the sensor data and performs rapid analysis and prediction within 0.009 milliseconds per data segment. The experimental results demonstrate that the model achieves a 99.8% accuracy in detecting wind turbine bearing faults within milliseconds of their manifestation.  
In the case of segments consisting of samples, the testing dataset was partitioned into 4800 individual segments, with each segment containing 32 samples. The proposed system demonstrates notable efficiency in processing sensor data, facilitating swift analysis and prediction within an impressive timeframe of 0.005 milliseconds per data segment. The experimental results substantiate the model's remarkable performance, yielding a detection accuracy of 99.7% in identifying wind turbine bearing faults within milliseconds of their manifestation.  
Both the Raspberry Pi and the desktop achieved identical outcomes by utilizing the proposed method. However, the testing times varied. The Raspberry Pi was significantly slower than the PC when it came to test speeds. The proposed method was significantly better, with respect to accuracy and execution time, compared to the comparison networks. The proposed method demonstrated superior performance and aligns with the specific requirements. Multiple models were chosen based on our requirements, i.e., for rapid speed, we selected 32 samples per segment, and for high precision, we chose 64 samples per segment. Each model was approximately 5 kb in size.