基于SVD降噪的经验模式分解及其工程应用

提出了一种基于奇异值分解降噪的机械设备振动型号经验模式分解方法,该方法首先对原始振动信号进行相空间重构和奇异值分解,然后根据分解奇异值的奇异熵确定降噪阶次,最后利用经验模式分解法提取降噪后振动信号的基本模式分量。对滤波前和滤波后的工业现场振动信号进行了经验模式分解,分析结果表明奇异值分解能够有效地提高信噪比,突出原始振动信号的故障特征,使得降噪后的振动信号分解出的基本模式分量具有更明确的物理意义,有利于对设备故障进行精确诊断。

EMPIRICAL MODE DECOMPOSITION AND ITS ENGINEERING APPLICATIONS BASED ON SVD DENOISING

A new empirical mode decomposition (EMD) method based on singular value decomposition(SVD) denoising is proposed for mechanical vibration signal analysis. Firstly, reconstruct the original vibration signal in phase space and decompose the attractor track matix by SVD, and then select a reasonable order for noise reduction according to the singular entropy of singular spectrum. Finally, decompose the denoised vibration signal by EMD to extract the intrinsic mode functions (IMFs).The method is applied in the decomposition of filtered and un-filtered vibration signal. The results show that SVD can effectively increase the signal noise ratio (SNR) and emphasize the fault characteristic of the original vibration signal.The IMFs extracted from the denoised signal have clear physical meaning and will increase the precision of fault diagnosis for mechanical equipments.