基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取

针对滚动轴承早期故障难以提取和最大相关峭度解卷积（maxim correlated kurtosis deconvolution，MCKD）降噪效果受滤波器长度L的影响，提出了基于互补集合经验模态分解（complementary ensemble empirical mode decomposition，CEEMD）和自适应最大相关峭度解卷积相结合的故障特征提取方法（CEEMD-AMCKD）.首先，利用CEEMD将信号分解得到一组固有模态分量，利用峭度值筛选出冲击成分明显的分量；然后，以排列熵值为标准，运用步长搜寻法确定最佳的MCKD滤波器长度，对前面筛选出的分量进行降噪处理；最后，将降噪后的分量及其他分量进行信号重构并根据包络功率谱提取故障特征频率.通过仿真和试验验证了该方法的有效性.

Fault diagnosis method for rolling bearing's weak fault based on minimum entropy deconvolution and sparse decomposition

It is difficult to extract the early fault of rolling bearings and the noise reduction effects of maxim correlated kurtosis deconvolution (MCKD) are affected by filter length L. A rolling bearings fault diagnosis method was put forward based on complementary ensemble empirical mode decomposition (CEEMD) and adaptive maxim correlated kurtosis deconvolution (AMCKD). First, a set of intrinsic mode function (IMF) components were decomposed by CEEMD, and the kurtosis was then used to select the IMF components that need to reduce noise. Then, the step size search method was used to select the best MCKD filter length according to the permutation entropy, and the components were processed by adaptive MCKD. Finally, the denoised components and other components were reconstructed, and the fault characteristic frequency was extracted according to the envelope power spectrum of the signal. The simulation signal and experimental data prove the effectiveness and advantages of the proposed method.