## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Data** & **Model** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** \\ \hline \multirow{3}{*}{A} & 1 DCNN-INPSO-SVM & 94.17 & 94.17 & 94.17 & 94.17 & 94.17 & 94.17 & 94.33 & 94.17 & 94.17 & 94.17 & 94.186 \\  & WPT-INPSO-SVM & 84 & 84 & 84 & 84.17 & 83.17 & 84 & 83.83 & 84.17 & 84 & 84 & 84 & 83.934 \\ \hline \multirow{3}{*}{B} & 1 DCNN-INPSO-SVM & 97.17 & 97.17 & 97.17 & 97.17 & 97.17 & 97.17 & 97.17 & 97.33 & 97.17 & 97.17 & 97.186 \\  & WPT-INPSO-SVM & 86.83 & 87.17 & 86.83 & 86.83 & 86.83 & 87 & 86.83 & 86.83 & 86.83 & 87 & **86.898** \\ \hline \multirow{3}{*}{C} & 1 DCNN-INPSO-SVM & 94.33 & 94.33 & 94.33 & 94.33 & 94.33 & 94.33 & 95.43 & 94.33 & 94.33 & 94.33 & 94.247 \\  & WPT-INPSO-SVM & 85 & 84.67 & 84.67 & 84.67 & 85 & 84.67 & 84.67 & 84.67 & 84.67 & 84.67 & 84.67 \\ \hline \multirow{3}{*}{D} & 1 DCNN-INPSO-SVM & 90.33 & 90.33 & 90.5 & 90.33 & 90.33 & 90.33 & 90.5 & 90.5 & 90.33 & 90.33 & 90.381 \\  & WPT-INPSO-SVM & 86.33 & 86.33 & 86.33 & 86.5 & 86.5 & 86.33 & 86.17 & 86.67 & 86.33 & 86.382 \\ \hline \multirow{3}{*}{E} & 1 DCNN-INPSO-SVM & 93.33 & 93 & 93.33 & 93.33 & 93.33 & 93.33 & 93.33 & 93.33 & 92.83 & 93.214 \\  & WPT-INPSO-SVM & 85.67 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.33 & 85.364 \\ \hline \multirow{3}{*}{F} & 1 DCNN-INPSO-SVM & 94.67 & 94.67 & 95.33 & 94.83 & 94.83 & 94.83 & 94.83 & 94.83 & 95 & 94.67 & **94.849** \\  & WPT-INPSO-SVM & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & 84.17 & **84.17** \\ \hline \multirow{3}{*}{G} & 1 DCNN-INPSO-SVM & 95.5 & 95.5 & 95.67 & 95.5 & 95.55 & 95.5 & 95.5 & 95.5 & 95.5 & 95.57 \\  & WPT-INPSO-SVM & 83.67 & 83.67 & 83.67 & 83.17 & 83.67 & 83.67 & 83.67 & 83.67 & 83.67 & 83.67 & 82.67 & **83.52** \\ \hline \multirow{3}{*}{H} & 1 DCNN-INPSO-SVM & 94.5 & 94.5 & 94.5 & 94.5 & 94.5 & 94.5 & 94.5 & 94.5 & 94.5 & 94.33 & **94.483** \\  & WPT-INPSO-SVM & 84.33 & 84.33 & 84.33 & 84.33 & 84.33 & 84.33 & 84.33 & 84.17 & 84.17 & 84.33 & **84.298** \\ \hline \multirow{3}{*}{I} & 1 DCNN-INPSO-SVM & 93.5 & 93.5 & 93.5 & 93.5 & 93.67 & 93.5 & 93.67 & 93.67 & 93.5 & 93.67 & **93.568** \\  & WPT-INPSO-SVM & 85.67 & 85.67 & 85.67 & 85.5 & 85.67 & 85.67 & 85.67 & 85.5 & 85.67 & **85.636** \\ \hline \multirow{3}{*}{J} & 1 DCNN-INPSO-SVM & 92 & 92 & 92 & 92 & 92 & 92 & 92 & 92 & 92 & 92 & **92** \\  & WPT-INPSO-SVM & 83.5 & 83.5 & 83.5 & 83.5 & 83.5 & 83.5 & 83.5 & 83.5 & 83.33 & **83.483** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recognition results of ten trials using different methods. However, the proposed 1 DCNN-INPSO-SVM does not use the temporal features of the vibration signals. The future work will focus on the extraction and utilization of the spatial-temporal characteristics. In addition, how to reduce the computational load and speed up the training process still deserve further study. Conceptualization, [PERSON] ([PERSON]) and X.Y. ; investigation, [PERSON] ([PERSON]), X.Y. and Y.S. ([PERSON]); methodology, Y.S. ([PERSON]) and X.Y. ; software, [PERSON] ([PERSON]); supervision, X.Y. and C.Z. ; validation, Y.S. ([PERSON]), X.Y. and Q.X. ; writing-original draft, Y.S. ([PERSON]) and X.Y. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (Grant No. 61803227, 61973184, 61773242, 61603214), the National Key R & D Program of China (Grant No.2017 YFB1302400), Independent Innovation Foundation of Shandong University (Grant No. 2082018 ZQXM005). We would like to thank the editors and the anonymous reviewers for their insightful comments and constructive suggestions. The authors declare that there is no conflict of interests regarding the publication of this paper.