双树复小波域MCA降噪在齿轮故障诊断中的应用

齿轮箱早期故障信号中往往包含强烈的干扰噪声,而基于简单阈值规则的小波系数降噪方法往往不能取得良好的效果.针对该问题,提出了基于形态分量分析(MCA)的双树复小波降噪方法.首先,对强背景噪声故障信号进行双树复小波变换,得到不同层的小波变换系数;然后,选取小波系数周期性较为明显层的小波系数进行MCA降噪;最后,将降噪后的系数进行单支重构后便可获得故障特征信号,对降噪信号进行包络分析便可以确定信号的故障特征频率.利用该方法对仿真分析和某轧机齿轮箱打齿故障早期信号进行了处理,结果表明:该方法能够在有效去除信号中的强背景噪声,比单独MCA降噪及软阈值降噪具有更好的效果,得到了更清晰的故障特征频率,从而为齿轮早期故障诊断提供了一种新方法. 

Denoising method based on dual-tree complex wavelet transform and MCA and its application in gear fault diagnosis

The vibration signals of gearbox incipient failure often contain strong noise, which results difficulty in fault feature extraction by the conventional denoising method, such as threshold based method.Thus, a new method based on dual-tree complex wavelet transform (DT-CWT) and morphological component analysis (MCA) was proposed.In the processing, the signal was firstly processed by DT-CWT to gain the coefficients of different layers.Secondly, MCA was employed to denoise the coefficient which was more periodic.Then, the denoised signal with weak fault feature could be gotten from a following single reconstruction.Finally, the fault characteristic frequency could be located accurately by simple envelope spectrum analysis.A simulate signal and incipient failure vibration signal of mill gearbox were processed using this method, and the results show that the method can remove the strong background noise in the signal effectively, and has better effect than single MCA and soft threshold method, and get a more clear fault characteristic frequency, thereby providing a new method for gearbox incipient fault diagnosis.