## 4 Conclusions and Future Work  
Given the non-linearity and non-stationarity of bearing vibration signals, a novel diagnosis method based on SSDAE is proposed in this paper. EEMD is used for adaptive multi-scale decomposition of the vibration signal to obtain the stable component. MPE, which can effectively characterize the working characteristics of bearings in different health conditions, is used for the representative feature extraction. The proposed SSDAE was used for further feature learning and health condition classification. CWRU bearing dataset containing four health conditions with three fault severities, and the laboratory measurement bearing dataset containing seven health conditions with single fault and compound fault were used to validate the proposed method. And for the two bearing dataset diagnosis, the average classification accuracy was as high as 99.55% and 97.98%, respectively. The proposed SSDAE stack structure was compared with other stack form, and it shows that the proposed structure  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Classification Accuracy (\%)**} \\ \cline{2-6}  & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Average Accuracy** \\ \hline EEMD + MPE + SSDAE & 97.9 & 98.19 & 98.38 & 97.43 & 97.98 \\ EEMD + MPE + Stacked AE & 96.86 & 95.9 & 96.19 & 94.48 & 95.86 \\ EEMD + MPE + SVM & 92.47 & 93.33 & 88.48 & 91.43 & 91.43 \\ Stacked AE & 90.09 & 89.62 & 89.24 & 90.47 & 88.07 \\ AE & 82.95 & 86.76 & 84.95 & 83.9 & 86.43 \\ SVM & 66.67 & 63.8 & 62.95 & 63.24 & 64.17 \\ BPNN & 37.24 & 33.71 & 38.57 & 35.33 & 36.21 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average classification accuracy (%) of the proposed method and the comparative methods.  
Figure 29: Diagnosis results of the four tests.  
is superior in terms of both reconstruction error and iteration time. The influence of several key parameters on diagnosis accuracy was optimized through experiments. The proposed method was compared with the comparable methods, and results demonstrated that our method is more effective.  
In the future, information about more bearing fault types needs to be collected and examined to enrich the fault database. For feature extraction, we only considered MPE method. MPE is based on the comparison between adjacent data, and extracts feature parameters reflecting signal randomness and dynamic mutation. Because it does not consider the data itself, it may ignore some information of the data itself. Hence, more efficient feature extraction methods could be developed for feature fusion to improve the classification accuracy. Generally speaking, most of the current fault detection methods are to predict the faults that have occurred and verify the effectiveness of the algorithm. However, the ultimate engineering application of fault diagnosis is to detect and predict the current faults. Therefore, online diagnosis of early health prediction on the basis of our research is worth studying.  
[PERSON] and [PERSON] were responsible for the overall work and proposed the idea and experiments in the paper. [PERSON] performed some of the experiments and contributed to many effective discussions regarding both ideas and paper writing. [PERSON] provided many useful suggestions and performed some of the experiments for the paper. [PERSON] made many useful suggestions and comments for the paper.  
This research was funded by the National Natural Science Foundation of China (Grant nos.51705531) and the Jiangsu Provincial Natural Science Foundation of China (Grant no. BK20150724).  
The authors declare no conflict of interest.