## 4 Experiment
### Results and Discussion  
In this paper, 60 fusion features from each bearing state were constructed from the collected vibration signals. Thirty labeled samples were used to construct an SVM model, and the other 30 samples were employed for testing. From the description above, a fusion feature extraction strategy, including sensitive IMF selection and feature fusion of multiple sensors, was proposed to diagnose bearing faults with an SVM model. The BP neural network is a frequently used artificial intelligence classifier for fault diagnosis [32]. Therefore, we first implemented the BP neural network as the classifier to diagnose bearing faults to testify the availabilities of the proposed method. When constructing the results of the BP neural network model, its four goal outputs, marked output1, output2, output3, and \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Conditions** & **IMF1** & **IMF2** & **IMF3** & **IMF4** & **IMF5** & **IMF6** & **IMF7** & **IMF8** & **IMF9** & **IMF10** \\ \hline S1 & 0.6672 & 0.6817 & 0.5388 & 0.1293 & 0.0328 & 0.0520 & 0.0124 & 0.0091 & 0.0093 & 0.0036 \\ S2 & 62.6646 & 28.4247 & 1.4968 & 1.2401 & 1.0390 & 0.1951 & 0.0190 & 0.0138 & 0.0102 & 0.0191 \\ S3 & 251.5411 & 2.1859 & 1.5832 & 0.4112 & 0.9304 & 0.0021 & 0.0194 & \(3.3\times 10^{-4}\) & \(1.1\times 10^{-5}\) & \(5.3\times 10^{-6}\) \\ S4 & 55.0147 & 50.8732 & 9.4266 & 0.7410 & 0.1520 & 0.0435 & 0.0083 & 0.0110 & 0.0043 & 0.0114 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CCRF values of IMFs decomposed from vibration signals collected by Sensor 6. Figure 10: Vibration signals collected from Sensor 6 and the decomposition results of EEMD: (**a**) normal IMFs; (**b**) IMFs of inner-race fault; (**c**) IMFs of ball fault; (**d**) IMFs of outer-race fault. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Conditions** & **Sensor 1** & **Sensor 2** & **Sensor 3** & **Sensor 4** & **Sensor 5** & **Sensor 6** \\ \hline S1 & 0.4214 & 0.0244 & 0.4368 & 0.0585 & 0.0184 & 0.0406 \\ S2 & 0.3958 & 0.0312 & 0.4183 & 0.0617 & 0.0580 & 0.0350 \\ S3 & 0.3897 & 0.0898 & 0.3949 & 0.0450 & 0.0446 & 0.0360 \\ S4 & 0.3548 & 0.0060 & 0.3434 & 0.1068 & 0.0789 & 0.1102 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Group weight values of six sensors under four conditions. output4, were coded with '0' and '1'. In this paper, the goal outputs of states S1, S2, S3, and S4 are represented by '1000', '0100', '0010', and '0001', respectively. Figure 11 displays the test results of the BP neural network. As can be observed from Figure 11a, output1, output2, output3, and output4 of these test samples with serial numbers 1-31 can be regarded as being close to '1', '0', '0', and '0', and they matched the goal outputs to some degree. However, the test results of the test samples with serial numbers 61-120 mismatched the goal outputs. In the same manner, the diagnosis results of Figure 11b-d can be analyzed. By contrastively analysing the plots in Figure 11, it can be observed that Figure 11d has the best classification effect, and the accuracy of Figure 11c is greater than that of Figure 11a,b. These experimental results demonstrate that the proposed sensitive IMF selection and fusion extraction method can improve the fault diagnosis accuracy. In this paper, SVM was used as the classifier for the bearing fault diagnosis. In the model construction stage, the target outputs of bearing states S1, S2, S3, and S4 were represented by digital coding '1', '2', '3', and '4', respectively. As 12 features can be extracted from each vibration signal of six sensors, \(12\times 6\) features can be available to the classifier if all original features of the six sensors for the fault diagnosis are used. Figure 12 displays the fault diagnosis results of the SVM model; the test results and goal outputs are represented by different colors and shapes. By comparing the values of these test results and goal outputs in Figure 12a, it can be determined that the test results and goal outputs of some test samples did not coincide.