基于参数优化特征模态分解的强背景噪声下滚动轴承故障诊断

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (21) : 107-115.

论文

基于参数优化特征模态分解的强背景噪声下滚动轴承故障诊断

施亦非，黄宇峰，王锋，石佳，张洁

作者信息 +

Fault diagnosis of rolling bearing under strong background noise based on POFMD

SHI Yifei, HUANG Yufeng, WANG Feng, SHI Jia, ZHANG Jie

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文章历史 +

摘要

为准确提取被强背景噪声掩盖的滚动轴承故障信息，提出一种参数优化特征模态分解 (parameter-optimized feature mode decomposition, POFMD)方法。首先，为解决特征模态分解 (feature mode decomposition, FMD)方法的输入参数依赖人工经验选取的问题，以平方包络谱峭度 (kurtosis of the square envelope spectrum, KSES)为权值，结合平方包络谱基尼系数 (Gini index of the square envelope spectrum, GISES)构建加权平方包络谱基尼系数 (weighted Gini index of the square envelope spectrum, WGISES)作为目标函数，通过优化算法确定FMD的最优参数组合；其次，为解决FMD的主模态分量难以选取的问题，通过计算所分解模态分量的KSES值选取主模态分量；最后，通过包络谱分析实现故障诊断。经仿真信号和实测信号分析，验证了POFMD在强背景噪声下滚动轴承故障诊断中的有效性。与变分模态分解 (VMD)、最大相关峭度解卷积 (MCKD)和谱峭度 (SK)相比，POFMD有更优越的故障特征提取性能。

Abstract

To accurately extract the rolling bearing fault information masked by strong background noise, a parameter-optimized feature mode decomposition (POFMD) is proposed. Firstly, to solve the problem of selecting input parameters based on empirical experience in the feature mode decomposition (FMD), the weighted Gini index of the square envelope spectrum (WGISES) was used as the objective function, which is constructed using the kurtosis of the square envelope spectrum (KSES) as the weight and combined with the Gini index of the square envelope spectrum (GISES), to obtain the optimal parameter combination for the FMD through an optimization algorithm; Secondly, To address the challenge of selecting the main mode component in the FMD, the main mode component was selected by calculating the KSES values of the mode components decomposed by the FMD; Finally, envelope spectrum analysis was used to achieve fault diagnosis. After analyzing the simulated and measured signals, the effectiveness of the POFMD in rolling bearing fault diagnosis under strong background noise is verified. Compared with the variational modal decomposition (VMD), maximum correlation kurtosis deconvolution (MCKD) and spectrum kurtosis (SK), the POFMD performs better in extracting fault features.

导出引用

施亦非, 黄宇峰, 王锋, 石佳, 张洁. 基于参数优化特征模态分解的强背景噪声下滚动轴承故障诊断[J]. 振动与冲击, 2024, 43(21): 107-115

SHI Yifei, HUANG Yufeng, WANG Feng, SHI Jia, ZHANG Jie. Fault diagnosis of rolling bearing under strong background noise based on POFMD[J]. Journal of Vibration and Shock, 2024, 43(21): 107-115

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