改进的小波相邻系数降噪方法及其在机械故障诊断中的应用

Improved Wavelet Denoising Using Neighboring Coefficients and Its Application to Machinery Fault Diagnosis

为了提取被强噪声淹没的表征机械故障的微弱冲击特征信号,提出一种改进的小波相邻系数降噪方法。改变传统小波相邻系数的收缩因子计算方法以便更好地提取冲击信号特征。进行不同信噪比的轴承仿真信号降噪试验,试验结果表明,改进的小波相邻系数降噪方法与传统的小波相邻系数降噪方法相比,可以更好地提取强噪声背景下的冲击信号特征。将改进的小波相邻系数降噪方法用于实测轴承早期故障特征提取中,试验表明提出的降噪方法要优于传统的小波相邻系数降噪方法,可以更有效地提取轴承早期故障的特征频率。

In order to extract weak impulse signal in heavy noise,an improved method based on wavelet denoising using neighboring coefficients is proposed.The scaling factor of wavelet neighboring coefficients is computed with a different method to better extract the impulsive feature.The denoising experiments of simulated signals of bearing with different signal-noise-ratios indicate that the method can extract the impulse feature in heavy noise.The experimental results of early rolling bearing fault feature extraction show that this method is better than the traditional neighboring coefficients denoising method.The proposed method can effectively extract the early fault feature of roller bearing and can extract the fault frequency of the bearing.