## 4 Discussion
### Transfer Learning of the Fault Diagnosis Algorithm  
In this study, the GPU server in the cloud based on the SAES-DNN algorithm was used to train the train simulation motor fault data set and obtain the universal motor fault diagnosis model. In the motor bearing data set of Case Western Reserve University [50], some fault data are similar to the fault types of the transfer learning source domain data set. Therefore, we used this data set to simulate the personalized motor bearing data collected at the edge end. Through the migration of universal model parameters in the cloud and the personalized adjustment and training at the edge, the model at the edge end completed the \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Diagnostic** & **Network** & **Learning** & **Minimum** & **Highest** & **Average** & **Average** \\
**Model** & **Depth** & **Rate** & **Accuracy** & **Accuracy** & **Accuracy** & **Loss Rate** \\ \hline MLNN & 4 & 0.1 & 61.5\% & 92.2\% & 85.7\% & 11.3\% \\ \hline CNN & 4 & 0.01 & 82.9\% & 94.6\% & 90.7\% & 7.62\% \\ \hline SAE-DBN [14] & 6 & 0.1 & 92.9\% & 97.3\% & 95.6\% & 5.56\% \\ \hline WDCNN [13] & 4 & 0.1 & 93.3\% & 97.2\% & 95.3\% & 6.41\% \\ \hline
**SAES-DNN** & 6 & 0.1 & 93.8\% & 98.1\% & 96.5\% & 5.43\% \\ \hline BP-MLNN & 4 & 0.1 & 78.6\% & 92.5\% & 89.5\% & 9.69\% \\ \hline \end{tabular}
\end{table} 
Table 1: Calculation of the training accuracy of different models in the same data set. Figure 5: A comparison chart of accuracy (**a**) vs. time consumption (**b**) of different models in different learning times under the same data set. For (**a**), every 10 learning cycles was a statistical index for calculating the accuracy of the learning model; for (**b**), the training time of the model was calculated for every 10 additional learning cycles. diagnosis and recognition of the motor bearing fault. Due to the high frequency of data acquisition used at the edge end, part of the data was intercepted as the input of the model to diagnose the bearing fault; however, by identifying the edge end model, the state of the motor bearing was quickly fed back. The appropriate transfer strategy was chosen by comparing fine tuning, freezing and training. The gradient design of the training samples in the target domain is shown in Table 2. In each experiment, 160 groups of untrained labeled data were selected as the verification set for verifying fault diagnosis accuracy. Fine tuning refers to applying the weight parameters of cloud training directly to the edge end data, using the edge end training data for model training at the edge end, and then updating the parameters to all layers. Freezing and training kept all layers frozen except for the parameters of the last SAE and the softmax fully connected layer. The training sample gradient set in Table 2 was used to train the model, and only the parameters of the last SAE and softmax layers were updated in the training. When using the transfer learning method, we first used the source domain samples (simulation samples) to train the improved SAES-DNN algorithm in the cloud, before finally obtaining a model with a fault prediction accuracy of >95%. All model parameters were transmitted to the edge controller. We used fine tuning [51] and freezing and training [52] for the migration learning; that is, the model parameters transmitted from the cloud were directly loaded at the edge. The initial model at the edge end was trained with the gradient of training samples set in Table 2, and parameters of all layers were updated in the training. Each experiment was repeated 10 times, and the final result was the average of the 10 experimental results, as shown in Table 3.  
By comparing the experimental verification and analysis, we can draw the following conclusions:  
1. When there is a small number of training samples, the application of transfer learning can significantly improve the diagnostic accuracy, especially when the number of samples is less than 120. 2.