基于小波包时延相关解调的滚动轴承故障诊断方法

Fault diagnosis of rolling element bearings based on the wavelet packet transform and the time-delay correlation demodulation analysis

针对滚动轴承故障冲击信号周期性强且容易被强烈的背景噪声淹没,提出基于小波包时延相关解调的分析方法,实现滚动轴承的故障特征提取及故障诊断。首先,以信噪比SNR和均方根误差RMSE为准则,对比小波包阈值的降噪效果,选取最优小波包参数,以增强振动信号的信噪比。然后将降噪后的信号进行重构。最后,通过时延相关解调分析提取去噪重构信号的故障特征。通过仿真和试验数据分析,验证了所提算法能够较准确地提取轴承故障特征频率。基于与包络分析和小波包络分析进行的对比,证明了所提方法能够获得更高精度的故障特征参数,进而更加有效地实现滚动轴承的早期故障诊断。

Since the fault impulse signals of rolling element bearings are periodical and easily to be immerged by background noise,a novel method of fault-based feature extraction and diagnosis is worked out based on the wavelet packet transform(WPT)and the time-delay correlation demodulation analysis.Firstly,both the signal-to-noise ratio(SNR)and the root-mean-square error(RMSE)serve as the criteria to select the optimal wavelet-packet parameters,and thus the SNR of the vibration signals is enhanced and the de-noising effect of the wavelet packet transform is evaluated.Then,the de-noised signals are reconstructed for further analysis.Finally,the fault features of the reconstructed signals are extracted by means of the time-delay correlation demodulation analysis.The results show that the fault characteristic frequencies are extracted with higher accuracy based on a series of simulation and experimental studies.The comparison results of wavelet envelope analysis and envelope analysis show that this method has more effectiveness and feasibility for fault diagnosis of rolling element bearings with higher accuracy.