## 3 Experiments and Analysis
### Experiment 2: The Laboratory Measurement Bearing Dataset
#### 3.2.4 Validation Results  
The most parameters of the proposed SSDAE in Experiment 2 are the same as that in Experiment 1, and the number of input and output layer neurons are 60 and 7, which is determined by the dimension of the input and the number of bearing health conditions. The extracted feature vector was divided into training and testing datasets, and was put into the SSDAE model for health condition diagnosis. The average accuracy of four tests is 97.98%. Figure 27 presents the confusion matrix of the third  
Figure 26: MPE of seven health conditions with different scale factors in Experiment 2.  
Figure 25: Spectrogram of an IRF+BF and its IMF Components. (**a**) Spectrogram of an IRF+BF signal sample; (**b**) Spectrogram of the eight-order IMFs.  
classification results using the proposed method (classification accuracy 1033/1050 = 98.38%). The result of Experiment 2 also shows that the proposed method based on MPE and SSDAE can effectively identity bearing faults.  
A comparative experiment of different stack order, Scheme 1 (SAE + DAE), with Scheme 2 (DAE + SAE) was also conducted in Experiment 2, to validate the superiority of the proposed SSDAE structure. And the experimental results were compared in the same experimental condition with Experiment 1. The test result of Scheme 1 and Scheme 2 under different iterations is shown in Figure 28. It can be seen that, as the number of iterations increases, the reconstruction error of scheme 1 decreases more quickly than that of scheme 2, and is always much smaller than that of scheme 2. Consistent with the result of Experiment 1, the result of Experiment 2 also indicates that Scheme 1 is effective in diagnosis of bearing vibration signals and it causes almost no loss of input information.  
In order to verify the effectiveness of the proposed method, the stacked AE, AE, SVM, and BPNN were also used for comparison. The network structure and parameters settings of each comparison method are the same as those in Experiment 1. In order to avoid the impact of contingency, each diagnostic method has been run four times. The detailed testing accuracy of each method under four tests is shown in Figure 29. The first three methods are applied with the inputs that are preprocessing  
Figure 28: Reconstruction error of the two schemes in Experiment 2.  
Figure 27: Confusion matrix diagram of the third test result using the proposed method.  
by EEMD and MPE, whereas the inputs of the remaining methods are all raw time-domain signals. Specific diagnostic accuracy and average accuracy are shown in Table 8.  
As illustrated by the figure and the table, the proposed method not only has higher average fault recognition rate, but also has better diagnosis stability than other methods. We find that methods based on signal processing (EEMD) and feature extraction (MPE) have higher classification accuracy than those without any processing. This shows that the proposed feature extraction method (EEMD+MPE) could effectively extract the volatility characteristics of different signals. We also observe that with the same signal processing and feature extraction method, methods based on SSDAE or stacked AE have higher classification accuracy than those based on AE, SVM and BPNN. It 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 large number samples 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 is more effective in diagnosis performance.