时频相关匹配法在信号提纯中的应用

Application of time-frequency distribution correlating and matching method in signal extraction

非平稳信号与噪声之间存在较强的时频耦合,这使得经典的消噪方法难以实现信噪的有效分离。在分析短时傅里叶变换(STFT)、Wigner Ville时频分布(WVD)、Chirplet时频变换三大时频变换方法的理论基础上,提出了一种采用时频相关匹配进行非平稳信号噪声抑制的算法。该算法将信号的WVD作为模版与STFT能量谱分布互相关处理,得到无交叉项干扰且具有较高时频分辨率的信号时频二维谱(简称为自谱窗WVD)。采用二维最小二乘拟合方法将被分析信号中的有用成分匹配成Chirplet基函数,并将其提取出来进行重构,达到信号提纯的目的。仿真结果表明,只需在自谱窗WVD的基础上进行次数不多的基函数匹配,就能将非平稳信号中的有用成分与噪声分离。应用结果也验证了经过提纯后的齿轮故障脉冲出现的平均周期与相应的故障特征频率吻合较好。

The time-frequency coupling relation between useful non-stationary components and noises bring great difficulties to the realization of de-noising for non-stationary signals,which can not be solved by classic de-noising method in time or frequency domain.The principles of short time Fourier transform（STFT）,Wigner-Ville transform,Chirplet adaptive decomposition are analyzed,and then a novel de-noising method for non-stationary based on joint time-frequency distribution is proposed.In this method,the analyzed signal WVD is seen as the combination of auto-WVD and cross-term WVD.Firstly,STFT energy spectrum of the analyzed signal is used as template to cross-correlate with its corresponding WVD in order to obtain the satisfactory time-frequency distribution with high time-frequency resolution and without cross-term interferences.Secondly,the useful components are decomposed as Chirplet function using the two-dimension least square fitting method,and then are extracted out to reconstruct for noise suppression.Finally,the computer simulation results verify the effectiveness of this proposed method.Its application in gearbox fault diagnosis indicates that with the method the extracted cycle of the gearbox vibration impulses has a good consistency with the corresponding fault frequency.