基于MED和SK的滚动轴承循环冲击特征增强
Cyclic Shock Enhancement by the Combination of Minimum Entropy Deconvolution and Spectral Kurtosis

提出了一种融合最小熵解卷积（minimum entropy deconvolution, 简称MED）和谱峭度（spectral kurtosis, 简称SK）的轴承循环冲击类故障检测方法。利用最小熵解卷积得到消噪信号，若能检测到轴承故障特征则完成诊断过程，否则对消噪信号进行谱峭度分析选取最佳滤波器参数，对滤波信号进行二次滤波。通过包络谱检测确定是否存在故障及故障类型。实验室信号及工程案例的分析结果验证了该方法在检测轴承局部故障中的有效性和优越性。
Localized faults in rolling bearings present in the form of cyclic shocks in vibrations. We proposed the minimum entropy deconvolution (MED) method to enhance the sharp parts of the shocks. Then, the spectral kurtosis (SK) method was employed to eliminate the remaining noise and to further enhance the cyclic impact. Experimental data and field cases verified the effectiveness and merits of the proposed method.