## 6 Conclusions  
The present study proposes a damage diagnosis approach to NC machine tools based on SSA and LS-SVM. First, the original data are transformed from one-dimensional data into high-dimensional signals using PSR. The SSA method is applied to extract stationary and non-stationary parts from multi-dimensional signals without the need for independency and without prior information of the source signals. Subsequently, the selected non-stationary components were analyzed for classification using LS-SVM. Ten dimensionless parameters in the time-frequency domain were extracted and used as inputs for LS-SVM. The proposed SSA + LS-SVM method was applied to NC milling machine tools. The results show that the proposed SSA + LS-SVM method outperforms the original LS-SVM, PCA + LS-SVM and SSA + LDA methods. The achieved accuracy of the proposed SSA + LS-SVM  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Testing Classes**} & \multicolumn{3}{c}{**Testing Result**} & \multirow{2}{*}{**Classification Accuracy**} \\ \cline{3-4} \cline{5-5}  & & **Normal Tool** & & **Mild Wear Tool** & **Severe Wear Tool** \\ \hline \multirow{2}{*}{SSA + LS-SVM proposed} & normal tool & 20 & 0 & 0 & 100\% \\  & mild wear tool & 0 & 16 & 4 & 80\% \\  & severe wear tool & 0 & 3 & 17 & 83\% \\ \hline \multirow{2}{*}{Original LS-SVM} & normal tool & 18 & 2 & 0 & 90\% \\  & mild wear tool & 4 & 13 & 3 & 65\% \\  & severe wear tool & 0 & 5 & 15 & 75\% \\ \hline \multirow{2}{*}{PCA + LS-SVM} & normal tool & 19 & 1 & 0 & 99\% \\  & mild wear tool & 5 & e & 1 & 70\% \\  & severe wear tool & 1 & 4 & 15 & 75\% \\ \hline \multirow{2}{*}{SSA + LDA} & normal tool & 14 & 6 & 0 & 70\% \\  & mild wear tool & 7 & 10 & 3 & 50\% \\ \multicolumn{2}{c}{severe wear tool} & 2 & 5 & 13 & 65\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result of SSA + LS-SVM, original LS-SVM, PCA + LS-SVM and SSA + LDA with testing sets.  
method was higher than 80%, whereas that of the original LS-SVM, PCA + LS-SVM and SSA + LDA was as low as 65%, 70% and 50%, respectively. Moreover, the proposed method can significantly distinguish between normal tools and wear tools, which cannot be realized through the other three methods. Furthermore, it is possible to apply the proposed method to other fault diagnosis areas, e.g., electric motors, in which the signals of bars of an induction motor are time varying, and non-stationary [39; 40].  
However, classification accuracy of the proposed SSA + LS-SVM method for mild wear and severe wear with testing sets is just 80% and 85%, respectively. Future work will focus on improvement of this accuracy. The stationary sources extracted by SSA will be utilized in future studies to improve the accuracy in identifying mild wear tools.  
The authors are grateful to the support from the National Science Foundation of China (Grant No. 51405346, 51505338), the Zhejiang Provincial Natural Science Foundation of China (No. LY17E050005), and the Wenzhou City Public Industrial Science and technology project of China (No. G20160015).  
[PERSON] and [PERSON] conceived of, designed and performed the experiments; [PERSON] developed the method; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.