基于小波阈值降噪滚动轴承信号的研究

摘要

分析了不同故障情况下滚动轴承故障信号频率的变化情况以及Penalty阈值降噪模型、Brige-Massart阈值降噪模型和缺省阈值降噪模型的特点.利用小波分析和三种阈值降噪模型对实际采集的轴承故障信号进行降噪,结果表明,Penalty阈值和缺省阈值降噪模型比Brige-Massart阈值降噪效果更好.文中还对降噪后的信号进行了频谱分析,获取了滚动轴承的特征值,便于判断滚动轴承发生的故障类型.%The changes of the rolling bearing frequency with various bearing fault and the features of penalty wavelet denoising model, brige-Massart wavelet denoising model and default wavelet denoising model are analyzed. The actual bearing fault signals are analyzed by wavelet and three wavelet thresholding denoising models.The result shows that penalty wavelet thresholding denoising and brige-Massart wavelet thresholding denoising are better than default wavelet denoising.Frequency spectrum analysis of denoised signal is performed to get bearing characteristic value and determine the type of rolling bearing failures.