## 1 Introduction
The fault feature information of weak faults is difficult to extract directly using the standard EEMD algorithm; hence, [PERSON] et al. [24] proposed a novel feature extraction strategy to detect bearing weak faults based on tunable Q-factor wavelet transform and traditional EEMD. Similarly, [PERSON] et al. [25] used EEMD and correlation coefficients to extract effective features by adaptive denoising analysis. In addition to the above studies, a discussion of other EEMD-based fault diagnosis methods can be found in Refs. [26; 27; 28]. Feature extraction is one step towards obtaining useful information regarding mechanical faults. A classifier must then be designed to complete the process of mechanical fault diagnosis. Many intelligent classification approaches have been presented for mechanical fault diagnosis. [PERSON] et al. [29] introduced artificial neural networks (ANN) to diagnose the gearbox faults of a marine propulsion system using the bi-spectrum technique. [PERSON] et al. Ref. [30] used Bayesian networks for the fault diagnosis of a wind turbine gearbox. In Ref. [31], fuzzy inference was employed to diagnose early bearing faults with morphological operators. In addition, other neural network-based methods were developed for mechanical fault diagnosis in Refs. [32; 33]. There are disadvantages of ANNs and drawbacks to back-propagation (BP) or radial basis function (RBF)-based fault diagnosis methods. For example, obvious over-fitting, low convergence rate, and especially poor generalization will appear if the number of the used training samples is small [34]. Therefore, support vector machine (SVM) was introduced as a new classifier to diagnose mechanical faults. SVM techniques are easier to use and address nonlinearity classification problems. Therefore, numerous SVM-based mechanical fault diagnosis strategies have been proposed [35; 36; 37]. In this paper, a new approach to information fusion is proposed for bearing fault diagnosis which fully uses the vibration signals of multi-sensors. Although this method is based on traditional tools, such as EEMD, SVM, and correlation coefficient analysis, a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis,and a new weight computation strategy is designed to fuse these features of multi-sensors. First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs) and the so-called CCRF is defined to select sensitive IMFs, and a new reconstructed vibration signal is obtained. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. Other parts of this paper are arranged as follows. The theoretical background, including the principles of EMD, EEMD, and SVM, are introduced in Section 2. Section 3 shows the proposed method, which includes sensitive IMF selection with the correlation coefficient ratio factor (CCRF), the feature fusion principle of multiple sensors, and fault diagnosis based on SVM. In Section 4, experimental research is performed on a multi-function mechanical fault simulator. The conclusions of this work are made in Section 5.