多小波自适应阈值降噪在故障诊断中的应用

Application of Multiwavelet Adaptive Threshold Denoising in Fault Diagnosis

为了提取淹没在强背景噪声下的微弱故障信息,引入多小波自适应阈值降噪方法实现滚动轴承的信号去噪,并结合包络解调提取故障特征.多小波具有多个尺度函数和小波函数,具备单小波无法同时满足的对称性、正交性、紧支性和高阶消失矩等优良特性,可匹配信号中的不同特征信息.基于轴承外圈点蚀故障的仿真信号,分别利用GHM多小波和Db2小波对其进行降噪处理.通过信噪比的定量分析表明,相比单小波而言,多小波的降噪优势明显.针对滚动轴承的微点蚀实验信号和现场实采集的工程数据,多小波自适应阈值技术比单小波方法具有更好的降噪效果,且更易于提取出滚动轴承的早期故障信息.

To extract the weak fault information submerged in strong background noise of the bearing vibration signal, multiwavelet denoising method with adaptive threshold and envelope demodulation method are applied in this paper. Due to several scaling functions and wavelet functions, multiwavelets have many excellent properties that single wavelet cannot satisfy simultaneously, such as symmetry, orthogonality, compact support, and high vanishing moments, which make it match different characteristics of analyzed signal. GHM muhiwavelet and Db2 wavelet are used to analyze the simulated outer race fault signal of rolling bearings, in which adaptive threshold selection strategy is introduced in multiwavelet denoising. Based on the comparison of denoising effects, multiwavelet adaptive threshold denoising is much more effective than single wavelet. Furthermore, muhiwavelet denoising method is applied to experimental signal and engineering data individually. Results show that the denoising method can identify the incipient fault feature as early as possible, which cannot be realized by single wavelet.