自适应多尺度形态学分析及其在轴承故障诊断中的应用

为解决强背景信号下冲击特征的提取问题,提出了一种自适应多尺度形态学分析方法.对于实际的待分析信号,分别定义长度尺度和高度尺度来确定多尺度形态学分析的结构元素,并基于信号的局部峰值实现自适应多尺度形态学分析.数值仿真实验分析表明,自适应多尺度形态学分析方法较单尺度形态学分析方法更利于提取信号的形态特征,避免了单尺度形态学分析在结构元素选择时的盲目性和对相关先验知识的依赖性.本文所提出的方法应用于轴承故障诊断,结果表明这种方法可以清晰地提取出各种特征信号.

Adaptive multiscale morphology analysis and its application in fault diagnosis of bearings

In order to solve the problem of impulsive features extraction from strong noise background, an adaptive multiscale morphology analysis (AMMA) algorithm was proposed. Corresponding to the analysis signal, the length scale and height scale were defined separately to select structuring elements for multiscale morphology analysis. An adaptive algorithm based on the information of local peaks of the signal was discussed. Numerical simulation experiments show that the proposed AMMA algorithm is better than the single-scale morphology analysis algorithm for extracting morphological features, and avoids the drawbacks of the ambiguity of selecting structuring elements and the dependence of empirical rules. The proposed AMMA algorithm is also examined in morphology analysis of the experimental signal measured from a bearing with faults. The results confirm that the proposed AMMA algorithm is able to extract various features clearly.