## 4 Experimental Verification
### Case Study I: Depending on Different Fault Components
#### 4.1.2 Parameter Selection  
The number of neurons in the input layer of the SAFC model was the same as the sample dimension, which was 200, the dimension of the sample spectrum. In the three hidden layers, the number of neurons was set as 600, 200, and 100. The number of neurons in the output layer was the same as the total number of health conditions. There was a total number of 20 training iterations for each layer. The learning rate was determined to be 1 E-4, while batch size was determined by parameter selection. After the data was output through the SAE network, the second hidden layer of SAE was used as the input layer of SF. The number of neurons in the input layer was also the same as the sample dimension. The number of neurons in the output layer was determined by parameter selection [23]. The test accuracy of the SAE part was taken as clustering accuracy, and the test accuracy of SF part as classification accuracy. First of all, the proportion of training samples in the SAE part was selected for research. Gear data was selected for testing, and the training batch size was tentatively set at 10. Meanwhile, the percentage of training samples in SF was set at 10%, and the output dimension was set at 200. In order to eliminate \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Group Number**} & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \multirow{3}{*}{Shaft} & Fault location & Normal & Crack on left side of the larger rotor & Crack on right side of the smaller rotor \\  & abbreviation & N & L1 & L2 & L3 & R1 & R2 & R3 \\  & Depth (mm) & 0 & 1.2 & 2.4 & 3.6 & 1.2 & 2.4 & 3.6 \\ \multirow{3}{*}{Bearing} & Fault location & Normal & Failure of bearing inner ring & Failure of bearing outer ring & \\  & abbreviation & N & I1 & I2 & I3 & O1 & O2 & O3 \\  & Depth (mm) & 0 & 0.2 & 0.6 & 1.2 & 0.2 & 0.6 & 1.2 \\ \multirow{3}{*}{Gear} & Fault location & Normal & Failure of planetary wheel & & Failure of sun wheel & \\  & abbreviation & N & P1 & P2 & P3 & S1 & S2 & S3 \\ \multirow{3}{*}{Gear} & Fault type & none & crack & pitting & wear & crack & pitting & wear \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of datasets. Figure 8: Time and frequency domain wave-forms of bearing signals with different health conditions. the effect of randomness, each group of tests were carried out 20 times. The diagnostic accuracy is shown in Figure 9. The results show that with the increase in the training sample size, the diagnostic accuracy increased continuously, and the time spent increased almost linearly. When the percentage of SAE training samples increased to 20%, the clustering accuracy reached 99.98% \(\pm\) 0.03%, and the classification accuracy reached 99.22 \(\pm\) 0.11%, after which both the two accuracy rates remained almost unchanged. Therefore, after weighing the accuracy of classification against the time spent, 20% was used as a percentage of the training sample in the following experiment. Then, after considering the selection of SAE training batch size, the diagnostic results of different batch sizes are as shown in Figure 10. It can be seen that when the batch size was reduced, the clustering accuracy and classification accuracy were slightly improved, but the average training time increased sharply with the decrease in batch size. When the batch size was 5, the training time was up to 153.27 s, which processed only one-third of the data that needed to be processed. When the batch size was 40, the time taken was 28.74 s, which was far less than 82.01 s when the batch size was 10, but the accuracy and variance were almost the same. Therefore, 40 was selected as the batch size. The parameters of the SF part were also taken into consideration after confirming the parameters of the SAE part. In order to study the effect of output dimension, 10% samples were still selected for testing. The diagnostic accuracy is shown in the figure.