The bearing fault signal can be seen as convolution of periodical impulses and interference components. The minimum entropy deconvolution (MED) is effective approach for the deconvolution of signal. However, the MED is vulnerable to random impulse and interference components. To solve the problem, an improved method, named minimum entropy morphological deconvolution (MEMD), is proposed in this paper. Firstly, the amplitude frequency response of two typical morphological operators (MOs) are discussed. These operators are then introduced into MED to filter the sample matrix. The optimal MO is selected based on the amplitude ratio of diagonal slice spectrum (DSS). Eventually, the filtered result is analyzed by DSS to identify the fault type. In MEMD, the influence of random shocks is eliminated and the scale of SE can be determined adaptively. The MEMD is verified by simulation and experimental signals. Comparison study is implemented and the analysis results verify its effectiveness and feasibility.

轴承故障信号可以看作是周期性脉冲和干扰分量的卷积。最小熵解卷积（MED）是信号解卷积的有效方法。然而，MED 容易受到随机脉冲和干扰成分的影响。针对该问题，本文提出了一种改进的方法——最小熵形态反卷积（MEMD）。首先，讨论了两种典型形态算子（MO）的幅频响应。然后将这些运算符引入 MED 以过滤样本矩阵。根据对角切片频谱 (DSS) 的幅度比选择最佳 MO。最终，DSS 分析过滤后的结果以识别故障类型。在 MEMD 中，消除了随机冲击的影响，可以自适应地确定 SE 的规模。MEMD 通过仿真和实验信号进行验证。进行了对比研究，分析结果验证了其有效性和可行性。