基于粒子群优化的改进EMD算法在轴承故障特征提取中的应用

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (16) : 182-187.

论文

郭泰1，邓忠民1，徐萌2

作者信息 +

An improved EMD algorithm based on particle swarm optimization and its application to fault feature extraction of bearing

Tai Guo1 Zhongmin Deng1 Meng Xu2

Author information +

文章历史 +

摘要

经验模态分解(Empirical Mode Decomposition, EMD）作为一种数据驱动的自适应信号分解方法，在轴承故障特征提取中有着广泛应用。针对EMD自身存在的模态混叠、端点效应以及三次样条插值带来的过冲/欠冲问题，同时考虑到有理Hermite插值方法具有一个形状控制参数，为选择最优的插值曲线提供了可能，基于此，本文提出了一种基于粒子群优化（Particle Swarm Optimization, PSO）的改进EMD算法，首先，选定频率带宽作为IMF优劣评判准则，并以此作为PSO的评价函数；其次在筛分过程中，从众多不同形状控制参数对应的分解结果中寻找最优IMF从而确定最优形状控制参数；最后，在每阶分解结果中都能保证所得IMF是最优的，从而达到更好的自适应性及更高精度。为验证所提出方法的有效性，采用传统EMD、EEMD与文中算法对仿真信号进行处理、对比，并通过计算相关技术指标进行了验证。最优将其应用于滚动轴承故障特征提取，并与传统EMD算法、EEMD进行对比，包络谱结果显示，改进后的EMD算法具有更好的分解效果，抑制干扰并能提取出更多故障信息。

Abstract

Empirical Mode Decomposition(EMD), as a data driven and adaptive signal decomposition method, was widely utilized in fault feature extraction of bearing. Aim to solve the problems of mode mixing, end effect and the overshoot or undershoot brought by cubic spline interpolation.Meanwhile, considering thatthe rational Hermite interpolation method has a shape controllingparameter which can change the shape of interpolation curve. An improved EMD algorithm based on Particle Swarm Optimization (PSO) and rational Hermite interpolation is put forward. Firstly, the frequency bandwidth,as the evaluation function of PSO, is used to selected optimal IMF; Secondly, find out the optimal IMF from many different decomposition results and determine the optimal shape controlling parameters; Finally, the obtained IMF is optimal in each step of the decomposition result. Therefore,better adaptability and higher accuracy can be achieved. To verify the effectiveness of presented method, a simulation signal is processed by traditional EMD, EEMD and improved EMD, respectively. The comparison results show that the introduced algorithm can validly restrain the mode mixing and the obtained IMF has better consistency with the real component. Eventually, the improved EMD is applied to fault feature extractionof rolling bearing and compared with the traditional EMD, EEMD, the envelope spectra indicate thatthe proposed algorithm has better decomposability, ability of restraining interference and can extractmore fault information.

导出引用

郭泰1，邓忠民1，徐萌2. 基于粒子群优化的改进EMD算法在轴承故障特征提取中的应用[J]. 振动与冲击, 2017, 36(16): 182-187

Tai Guo1 Zhongmin Deng1 Meng Xu2. An improved EMD algorithm based on particle swarm optimization and its application to fault feature extraction of bearing[J]. Journal of Vibration and Shock, 2017, 36(16): 182-187

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