基于正态反高斯模型的自适应小波消噪方法

提出一种基于正态反高斯分布模型局部逼近小波系数的降噪算法。该算法以db5小波作为振动信号的分解小波,对噪声信号进行分解。对于分解过程中包含大量噪声的小波系数,利用具有良好细节逼近性能的正态反高斯分布构造先验模型,在先验模型的基础上,运用贝叶斯最大后验概率估计从含噪的小波系数中估计出真实的小波系数。在后验估计的过程中,对于估计模型中的关键系数采用粒子群算法进行优化选取。利用估计的小波系数来重构信号,得到降噪后的信号。通过仿真实验和实际轴承的故障信号对该方法进行了验证,结果表明,该方法具有较好的降噪效果,可以有效的消除信号的噪声。

Adaptive Wavelet De-noising Algorithm based on Normal Inverse Gaussian Model

A locally adaptive wavelet de-noising method based on normal inverse Gaussian modal is proposed.Firstly,the db5 wavelet is used to decompose the signal.For those wavelet coefficients which contain a lot of noise,the normal inverse Gaussian modal with good approximation property is constructed as the prior distribution model of those coefficients,on the basis of the model,Bayesian maximum a posteriori estimator is used to estimate the noisy wavelet coefficients and got the realistic wavelet coefficients.Then in the process of posteriori estimation,in order to get the best posteriori approximation model,the particle swarm optimization algorithm is used to select the key coefficient of the model.Finally,new wavelet coefficients are used for the reconstruction of the de-noised signal,and the de-noised signal is gotten.The algorithm is analyzed by simulation and bearing fault signal respectively.Analysis results show that this algorithm has good noise reduction effect,and can efficiently reduce the noise.