###### Abstract
In recent years, as a new emerging signal processing method, variational mode decomposition (VMD) [22; 23] has been used widely in denoising vibration signals of rotating machinery. However, as the two key parameters of VMD, the penalty factor and the number of modes should be selected appropriately, which inferences the denoising result of VMD directly [13]. Morphological filtering (MF) is an important nonlinear filtering method based on mathematical morphological transformation [24]. MF uses the same structure elements (SEs) as the filtering window to match the signal to be analyzed or modify the signal locally in the time domain [25] and the information is preserved when the local morphological features of the analyzed signal match the SEs. Its working principle is simple and the calculation theory is efficient. Initially, MF was used widely in other signal processing fields such as the image signal and power signal [26]. MF could extract the edge contour and the shape features of the vibration signal effectively and realize the retention of fault features and the removal of interference noise. Recently, various examples in the literature have been emerging on the application of MF in mechanical fault diagnosis and various satisfactory results have been achieved. However, the filtering effect of MF in the fault diagnosis area is mainly determined by the selection of the scale and the shape of the SE of MF; different kinds of corresponding research have been carried out. An empirical rule was provided in [27] for choosing the shape and scale of SE. Filter the original signal in different scales and their corresponding weighted average is calculated to alleviate the scale selection influence of SE [28; 29] and its defects include that its process is time-consuming and fault-unrelated components might be contained [30]. Though some research [31; 32; 33; 34] reducing the fault-unrelated components problem existed in [28; 29], the problem of low computation effect was still not resolved. To enhance the adaptive ability of MF in the feature extraction of rolling bearing, an AMF with simple theory and high calculation efficiency is used as pre-processing step of the proposed FBS in the paper. The main contributions of the paper are as follows: (1) The impulse characteristic components buried in the original multi-component vibration signal of faulty bearing is enhanced by the AMF algorithm adaptively. (2) SI is introduced in the FBS algorithm, which has more robustness to noise and random impulses. (3) The feature extraction double enhancement method for the rolling bearing's weak fault by combining the AMF and FBS is proposed in the paper, whose feasibility and effectiveness are verified through one experiment vibration signal and one engineering vibration signal, respectively. The organizations of the remaining paper is as follows: the calculation processes of AMF and FBS are presented in Sections 2 and 3, respectively. The overall flow chart of the proposed method and its details are elaborated in Section 4. The effectiveness of the proposed method is verified through an experimental signal and an engineering signal in Section 5. The comparison study is carried out in Section 6 and the conclusion is obtained in Section 7 at last.