## 3 Experiments and Analysis
### Experiment 1: Case Western Reserve University (CWRU) Bearing Dataset
#### 3.1.4 Validation Results
Figure 18: Classification accuracy of the proposed method with different sparsity parameters. In order to know the layer-by-layer feature extraction effect of the SSDAE, the dimension reduction technology of t-SNE (t-distributed stochastic neighbor embedding) is used to reduce the dimension of each layer feature of a test set to 2 dimensions and visualize it [41]. The visualization results are shown in Figure 20. It can be seen that in the scatter plot of input data, the four states of samples are basically mixed together, and the degree of sample aggregation is poor. For each hidden layer, the features of each category are aggregated once. After the second hidden layer, each type has been basically separated. This shows that the feature distribution is greatly improved by SSDAE layer by layer feature extraction, and the ability of feature expression and distinction is stronger, thus ensuring the effective realization of subsequent state recognition and classification. Commonly used classification models, such as the traditional stacked AE, SVM, and back-propagation neural network (BPNN), were used to validate and compare the effectiveness of the proposed SSDAE. The stacked AE has the same structure as the SSDAE. The input, hidden, and output dimensions of the BPNN were 200, 100, and 4, respectively, and sigmoid was used as an activation function. The SVM used the Gaussian kernel function. As shown in Table 4, the four  
Figure 19: Classification accuracy of the proposed method with different corruption levels. Figure 20: Visualization results of each layer. methods used the same signal preprocessing (EEMD) and feature extraction (MPE) models in addition to the classification models mentioned above. The classification results of each health condition and the total classification accuracy are shown in Figure 16:0 The results demonstrate that the proposed method achieved the highest classification accuracy (99.6%), and the classification accuracy of each condition was also higher than the others. By comparing the first two shallow architecture methods with the last two deep learning methods, we found that, in the case of the same signal processing and feature extraction methods, methods based on the SSDAE or the stacked AE have higher classification accuracy than SVM and BPNN. This shows that deep learning method has a stronger ability in terms of feature learning and abstraction than shallow intelligent diagnosis method, especially in the case of a large number of samples with high-dimension and multi-classification issues. In addition, the SSDAE-based method achieved a higher accuracy than the stacked AE-based method, which proves that the proposed SSDAE outperforms the traditional stacked AE in extracting more representative features. To further verify the effectiveness of the proposed feature extraction method, wavelet packet-empirical mode decomposition (WP-EMD) [42], variational mode decomposition and permutation entropy (VMD-PE) [43], and improved empirical mode decomposition energy entropy (EMDEE) [44] were considered for comparison. The features extracted by the above methods were input into the SSDAE for classification, and the classification accuracies are shown in Figure 16:1 The proposed method achieved the highest total classification accuracy and performed better in most health conditions. Through the comparison, we observed that the feature extraction methods based on PE have higher classification accuracy than the EMD-based methods. This shows that MPE could effectively characterize and extract the volatility characteristics of different signals and mine the representative features for further diagnosis.