基于形态小波理论和双谱分析的滚动轴承故障诊断

为了探索基于双谱估计流程的对包含高斯和非高斯成分的故障信号进行有效处理的方法,将非线性小波-形态小波引入到振动信号处理中来,并进一步完善形态小波理论,提出形态小波包分解与重构算法.通过形态小波包软阈值降噪实现非高斯信号高斯化处理,在此基础上,发展了3种形态小波包-双谱分析级联算法:形态小波包-双谱估计、形态小波包-Hilbert-双谱估计、Hilbert-形态小波包-双谱估计,该方法对故障信号的高斯和非高斯成分都能有效抑止,尤其是Hilbert-形态小波包-双谱估计算法,适应性强,处理效果更好.经过滚动轴承故障诊断试验,结果表明,基于形态小波包和双谱分析的算法,能够准确区分不同类型、不同程度的故障序列,并取得了良好的效果.

Rolling bearing fault diagnosis based on morphological wavelet theory and bi-spectrum analysis

The nonlinear wavelet-morphological wavelet is introduced into the field of vibration signal processing to explore methods that can process effectively both Gauss noise and non-Gauss noise based on the routine of bi-spectrum analysis. Morphological wavelet packet was developed and its analysis and synthesis steps were described,to perfect the morphological wavelet theory. On the basis of excellent de-noising effects of morphological wavelet packet,three kinds of cascaded algorithms were put forward based on...