## 3 Experiments and Analysis
### Experiment 1: Case Western Reserve University (CWRU) Bearing Dataset
#### 3.1.4 Validation Results
used to extract a better feature description of the input, and the DAE is then used to enhance the robustness of feature extraction and reduce the generation gap between the training set and the testing set. Therefore, the reconstruction error of Scheme 1 was smaller. Thus, Scheme 1 would help improve the classification accuracy. Figure 16 shows that the time required of the two schemes decreases with the increase in iterations. Each iteration time of Scheme 1 (SAE + DAE) and Scheme 2 (DAE + SAE) tends to stabilize after about 120 iterations and 80 iterations, respectively, mainly because the network has been fully trained. However, regardless of the number of iterations, Scheme 1 takes less time than Scheme 2, and each iteration time is stable at about 1s and 5s, respectively. The possible reason for this finding is that, in our experiment, the input of the SSDAE is a high-dimensional vector, and the proposed Scheme 1 starts training the SAE first, which ensures that the dimension reduction is implemented at the beginning of network training. Therefore, the network could be trained quickly from the low-dimensional vector, so each iteration time is reduced. Some key parameters considerably influence classification accuracy or time consumption, such as the number of hidden layer neurons, the sparsity parameter, and the corruption level. Therefore, to analyze the impacts of different parameters on the diagnosis result, experiments for each parameter with different values are carried out for comparison. The number of hidden layer neurons is important for the feature learning and classification accuracy of the SSDAE. Figure 17 shows the relationship between the number of neurons in two hidden layers with classification accuracy and time consumption. Considering dimension reduction, the  
Figure 16: The contrast chart of the iteration time between the two schemes. Figure 15: Reconstruction error of the two schemes. number of neurons in the second hidden layer was set to be less than that in the first hidden layer, but equal or a little more than half of that in the first layer. Figure 17 shows that the classification accuracy improves gradually as the number of neurons increases. Notably, when the number of neurons in the first layer is more than 100, the classification accuracy tends to be stable, but time consumption increases linearly. Therefore, to ensure both high classification accuracy and quicker calculation speed, the number of neurons in the two hidden layers was selected as 100 and 60, respectively. A proper sparsity parameter not only improves the feature learning ability of the network but also improves the computing efficiency. The sparsity parameter is usually a small value that is no greater than 0.5. So, the performance with sparsity parameters varying from 0.05 to 0.5 was studied in our experiments. The experimental results are shown in Figure 18. Figure 18 shows that with the increase in sparse parameter values, the classification accuracy increases gradually and then decreases gradually. When the sparsity value is greater than 0.25, the diagnosis accuracy obviously decreases. Therefore, the best diagnosis performance is obtained when the sparsity parameter is 0.15. An excessive corruption level value may excessively remove useful information, and too small a value will affect the filtering of redundant information, thus affecting the diagnosis accuracy. The effect on classification accuracy of different corruption level values is shown in Figure 19. With the increase in the corruption level value, the classification accuracy increases gradually, and then decreases gradually. When the corruption level value is greater than 0.2 and less than 0.35, stable high classification accuracy rates are observed. When the corruption level value is greater than 0.35, the classification accuracy obviously decreases. Therefore, the best diagnosis performance is obtained when the corruption level is 0.3. Figure 17: Effect of the number of neurons in two hidden layers.