针对多分量、强背景噪声下滚动轴承故障特征提取困难的问题，提出了将改进傅里叶模态分解（Modified Fourier mode decomposition，MFMD）和频带熵（FBE）分析相结合的滚动轴承故障特征提取方法。针对傅里叶分解（FDM）在强背景噪声下边界频率偏移和过分解的问题，提出了频带熵和包络谱相结合的敏感频带和敏感模态分量选取方法。首先，通过FBE分析选取频带熵区域极小值处作为敏感频带中心频率并确定敏感频带边界。然后，在敏感频带区间内对信号进行带限傅里叶模态分解，获得若干个相互正交的傅里叶本征模态函数（Fourier intrinsic mode function，FIMF）和其边际希尔伯特谱；接着，根据FIMFs与原信号的频带熵的区域从属关系选取出能够反映故障特征的敏感FIMFs；最后，对选取的FIMF进行包络谱分析提取出故障特征。将该方法应用到轴承仿真数据和实验数据中，能够实现轴承故障的精确诊断，证明了该方法的有效性和优越性。
In order to solve the problem that it is difficult to extract fault features of rolling bearings under multi-component and strong background noise, a rolling bearing fault feature extraction method based on improved Fourier mode decomposition (MFMD) and band entropy (FBE) analysis is proposed. In order to solve the problem of boundary frequency offset and over decomposition of Fourier decomposition (FDM) under strong background noise, a method for selecting sensitive frequency bands and sensitive mode components based on the combination of band entropy and envelope spectrum is proposed. First of all, through FBE analysis, the minimum of band entropy is selected as the center frequency of the sensitive band and the boundary of the sensitive zone is determined. Then, the signal is decomposed by band-limited Fourier mode decomposition in the sensitive frequency band, and several mutually orthogonal Fourier intrinsic mode function (FIMF) and their marginal Hilbert spectra are obtained. then, the sensitive FIMFs; which can reflect the fault characteristics is selected according to the regional dependency relationship between FIMFs and the frequency band entropy of the original signal. Finally, the selected FIMF is analyzed by envelope spectrum analysis to extract fault features. When the method is applied to the bearing simulation data and experimental data, the accurate diagnosis of bearing faults can be realized, which proves the effectiveness and superiority of the method.