自适应小波阈值滚动轴承故障振动信号降噪方法

Adaptive Wavelet Threshold Rolling Bearing Fault Vibration Signal Denoising Method

对于机械轴承故障振动信号的降噪处理,现有的小波阈值降噪方法不能满足提取轴承微弱故障振动信号的要求。针对硬、软阈值小波降噪方法存在间断点和恒定偏差以及现有的阈值函数灵活性较差等问题,提出了一种自适应的小波阈值函数来对滚动轴承故障振动信号进行降噪。以西储大学的轴承数据进行实际工程的仿真分析,通过对不同的阈值函数的信号降噪曲线以及频域信号的细化谱的降噪图像比对发现,自适应的小波阈值函数能够更好的滤除噪声,提取微弱的振动信号。

For the noise reduction processing of mechanical bearing fault vibration signals,the existing wavelet threshold noise reduction method can not meet the requirements of extracting weak vibration signals of bearings.Aiming at the problems of hard threshold and soft threshold wavelet denoising methods with discontinuity and constant deviation and poor flexibility of existing threshold functions,an adaptive wavelet threshold function is proposed to denoise the rolling bearing fault vibration signal.In this paper,the bearing data of Western Reserve University is used to simulate and analyze the actual project.Compared the noise reduction images of the frequency domain signal refinement spectrum with different threshold functions,the adaptive wavelet threshold function can better filter out the noise and extract the weak vibration signal.