## 5 Conclusions  
To fully use the vibration signals of multi-sensors, a new information fusion approach was proposed for bearing fault diagnosis. This method employs ensemble empirical mode decomposition (EEMD) as the tool for vibration signal processing, correlation coefficient analysis as the method for sensitive IMFs selection and feature fusion, and support vector machine (SVM) as the classifier. Considering that the traditional signal processing method cannot deal with the nonlinear and non-stationary vibration signals generated by these bearings with various conditions, the EEMD algorithm was used to decompose these original signals into a set of intrinsic mode functions (IMFs). Not all components are helpful for fault diagnosis, and some will even interfere with fault diagnosis,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiments** & **Features per Sample** & **Test Samples** & **Accuracy(\%)** \\ \hline SVM + features of vibration signal & \(12\times 6\) & 30 & 73.33 \\ SVM + features of first IMF & \(12\times 1\) & 30 & 74.17 \\ SVM + features of reconstruction signal & \(12\times 6\) & 30 & 95.83 \\ Proposed method & \(12\times 1\) & 30 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed method with commonly applied classifiers.  
Figure 12: Test results of the support vector machine (SVM) model where the features used were extracted from (**a**) the vibration signals of six sensors; (**b**) the first IMF of six sensors; (**c**) reconstructed vibration signals using the proposed sensitive IMFs collection method; (**d**) reconstructed vibration signals using the proposed feature fusion method.  
so a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reduce noise and reconstruct new vibration signals for further analysis. After that, an original feature space was constructed from the reconstructed signal, and the so-called fused features were extracted by original feature space and weights assigned by correlation coefficients among these vibration signals of considered multi-sensors. With this feature fusion algorithm, the dimension of final features was successfully reduced to one-sixth of the original feature space. Experiments were implemented to verify the performance of the proposed approach. The experimental results of the BP neural network showed that the proposed sensitive IMF selection and fusion extraction method can improve the fault diagnosis accuracy. The diagnosis results of original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method were 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments showed that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve the diagnosis results for bearings.  
[PERSON] designed the experiments and analyzed the datasets; [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed part of the dataset; [PERSON], [PERSON] and [PERSON] wrote the paper. All authors contributed to discussing and revising the manuscript.  
This work is supported by the National Natural Science Foundation of China (Nos. 51605478, U1510205, 51475455), the Natural Science Foundation of Jiangsu Province (No. BK20160251), the Fundamental Research Funds for the Central Universities (2017 QNA17), and the project was funded by the Priority Academic Program Development of the Jiangsu Higher Education Institutions (PAPD).  
The authors would like to thank all of the reviewers for their constructive comments.  
The authors declare no conflict of interest.