# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 5 Experimental Validation
### Results and Analysis
#### 5.4.4 Performance Comparison of Edge Node Embedding  
To validate the advantages of deploying the CNN-DS method on edge nodes, this study conducted a verification and analysis of the running speed and accuracy of CNN-DS in both edge and cloud environments. Cloud Server 1 has better computing resources than \begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **Average Accuracy (\%)** & **Rapidity (s)** & **Stability (\%)** \\ \hline CNN-DS & 99.71 & 63.1 & 0.059 \\ CNN & 97.36 & 54.6 & 0.082 \\ RNN & 92.13 & 135.6 & 0.043 \\ DT & 54.80 & 27.5 & 0.48 \\ RF & 79.40 & 35.7 & 0.35 \\ CNN-SVM & 99.39 & 72.5 & 0.076 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison with other machine learning methods. Figure 11: Performance comparison before and after CNN-DS fusion: (**a**) the confusion matrix for temperature data diagnosis; (**b**) the confusion matrix for optimized dual-modal data diagnosis. the edge side and Cloud Server 2 has better computing resources than Cloud Server 1. The network configuration of the cloud and edge nodes is shown in Table 8.  
Running speed evaluation  
This study selected 100 sets of data to compare diagnostic speed and the experimental results are shown in Figure 12. Green represents the diagnostic execution speed when deployed on Cloud Server 1, with an average speed of 58.6 s/case. Purple represents the upload speed of Cloud Server 1. Pink represents the diagnostic execution speed when deployed on the edge node, with an average speed of 63.1 s/case. Black represents the diagnostic execution speed when deployed on Cloud Server 2, with an average speed of 51.4 s/case. Blue represents the upload speed of Cloud Server 2. As shown in Figure 12, the diagnostic speed in the three cases follows the order: Cloud Server 2 > Edge Node > Cloud Server 1 (from fastest to slowest). When the data upload time is not considered, Cloud Server 2, which has the best computational resources, achieves the fastest execution speed at 51.4 s/case, while the edge node is the slowest. However, when considering the data upload time, the edge node outperforms Cloud Server 1 in overall speed since it does not require data transmission. This phenomenon occurs because, although Cloud Server 1 has slightly higher computational performance than the edge node in this experiment, its speed is negatively impacted by the need for data upload, making it slower than the edge node. In contrast, Cloud Server 2, with its superior computational performance, achieves a higher processing speed than edge computing. These results indicate that, within a certain range of computational resources, the diagnostic efficiency of edge nodes surpasses that of cloud computing, significantly improving fault diagnosis efficiency. Figure 12: Evaluation of CNN-DS running speed on edge and cloud. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **CPU** & **Memory** & **Bandwidth** & **RTT** \\ \hline Cloud Server 1 & \begin{tabular}{c} ECS General Purpose Instance; \\ ecs.t5 \\ \end{tabular} & 8 GB & 1 Gbps & 50 ms \\ Cloud Server 2 & \
\begin{tabular}{c} ECS Compute Optimized Instance; \\ ecs.c6 \\ \end{tabular} & 16 GB & 1 Gbps & 50 ms \\ Edge Node &
\begin{tabular}{c} NVIDIA Carmel ARMv8.2 64-bit \\ CPU 1.5 GHz \\ \end{tabular} & 8 GB & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Network configuration. * Accuracy evaluation In this study, a total of 20 sets of data were selected to compare diagnostic accuracy. Experiments were conducted in three different scenarios: Cloud Server 2, Edge Node, and Cloud Server 1. The experimental results are shown in Figure 13.  
In the figure, the red color represents the diagnostic accuracy of the edge node, while the blue and black colors represent the diagnostic accuracy of Cloud Server 1 and Cloud Server 2, respectively. It can be observed that the accuracy in all three scenarios is identical, reaching 99.71%. This study utilizes the TCP/IP protocol, which means that, even with different computing resources, the accuracy of the same model remains the same.