基于优化SSA-VMD的滚动轴承故障信号降噪方法

Noise reduction method of rolling bearing fault signal based on optimized SSA-VMD

针对滚动轴承故障信号降噪的问题,提出了一种基于优化麻雀搜索算法和变分模态分解的降噪方法。该方法利用优化麻雀搜索算法在既定范围内对变分模态分解的相关参数进行寻优,得到输入信号的最佳分解结果,根据时域相关系数选择有效的分量重构输入信号,从而实现对输入信号的降噪,通过滚动轴承的仿真故障信号和实际故障信号两方面分别验证该方法的有效性。结果表明,该方法相较于传统的变分模态分解方法拥有更好的降噪效果。

A noise reduction method based on optimized Sparrow Search Algorithm and Variational Mode Decomposition is proposed to address the problem of denoising rolling bearing fault signals.This method uses the improved sparrow search algorithm to optimize the relevant parameters of the variational mode decomposition within the given range,obtain the optimal decomposition result of the input signal,and selects the effective component according to the time domain correlation coefficient to reconstruct the input signal and achieve noise reduction of the input signal.The effectiveness of this method was verified through simulation fault signals and actual fault signals of rolling bearings.The results show that the method has better noise reduction performance compared to traditional variational modal decomposition method.