基于Morlet小波与尺度空间的滚动轴承故障诊断研究

Research on Fault Diagnosis of Rolling Bearing Based on Morlet Wavelet and Scale Space

针对滚动轴承早期故障特征提取困难的问题,提出了一种基于Morlet小波与尺度空间的滚动轴承故障诊断方法。该方法先用尺度空间划分频带边界,得到共振频带,再把频带边界信息代入Morlet小波中构造滤波器组对信号进行滤波。由于尺度空间划分频带边界存在过分割的问题,引入了包络相关峭度作为指标,提出采用尺度空间优化谱的方法来识别故障的最优共振频带,用仿真信号和台架试验获得的轴承故障信号验证了该方法的有效性,并与快速谱峭度进行了对比。结果表明,基于Morlet小波与尺度空间的滚动轴承故障诊断方法可以准确地识别最优共振频带,实现轴承故障诊断,同时诊断效果明显优于快速谱峭度指标。

Aiming at the difficulty of extracting the early fault features of rolling bearings,a rolling bearing fault diagnosis method based on Morlet wavelet and scale space was proposed.The frequency band boundary was divided by the scale space to obtain the resonance frequency band,and the frequency band boundary information was substituted into Morlet wavelet to construct a filter bank to filter the signal.Due to the problem of over-segmentation of the scale space division frequency band boundary,the envelope correlation kurtosis was introduced as an index,and a scale space optimization spectrum method was proposed to identify the optimal resonance frequency band of the fault.The validity of the method was verified by simulation signals and bearing fault signals obtained from bench tests,and compared with the fast spectral kurtosis method.The results showed that this method could accurately identify the optimal resonance frequency band and realize bearing fault diagnosis.At the same time,the diagnosis effect was significantly better than fast spectral kurtosis index.