分层自适应小波阈值轴承故障信号降噪方法

Bearing fault signal denoising method of hierarchical adaptive wavelet threshold function

针对轴承振动信号易受到噪声干扰的问题,提出了一种分层自适应小波阈值降噪方法。首先将轴承振动信号进行小波分解,获得各分解层的小波系数;之后保留低频信号的小波系数,对高频信号的小波系数进行分层自适应阈值处理;最后将阈值处理后的小波系数进行小波重构,得到降噪后的信号。通过构建一种在阈值处连续且在小波域内可导的分层自适应阈值函数,可以改进传统阈值函数重构偏差和过度降噪的缺陷。轴承故障仿真信号的降噪实验结果表明,该方法的信噪比和均方根误差均优于其他方法,有更好地降噪效果;机械故障模拟实验台的轴承故障信号降噪实验结果表明,该方法在降噪的同时保留了更多的故障信息,能够有效提升故障诊断率,更有利于轴承故障信号的降噪。

To solve the problem of noise interference in bearing vibration signals,a hierarchical adaptive wavelet threshold function denoising method is proposed.First,the bearing vibration signal is decomposed into wavelet coefficients obtaining the wavelet coefficients of each decomposition layer.After that,the wavelet coefficients of low frequency signals are retained and the wavelet coefficients of high frequency signals are processed by hierarchical adaptive thresholding.Finally,the wavelet of coefficients after threshold processing is reconstructed to get the denoised signal.By constructing a hierarchical adaptive threshold function that is continuous at the threshold and derivable in wavelet domain,the defects of the reconstruction deviation of traditional threshold function and excessive noise reduction can be improved.There is a trend of the threshold function parameter,affected by the occupying ratio of noise energy.By adjusting this parameter,the threshold function can be adaptively obtained in each wavelet decomposition layer to achieve more effective denoising effect.The simulation results of the noise reduction for bearing fault simulation signal show that the signal-to-noise ratio(SNR)and the root mean square error(RMSE)of the proposed method are better than others,and has better noise reduction effect as well.The experiments of on the mechanical fault simulation test rig show that this method saves more fault information while removing noise,and thus improves the fault diagnosis rate for the noise reduction of bearings.