基于优化VMD与改进阈值降噪的滚动轴承早期故障特征提取

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (13) : 146-153.

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陈鹏1,赵小强1,2,3

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摘要

针对复杂工况和强背景噪声干扰下，滚动轴承早期故障信号微弱导致故障特征难以提取的问题。提出了优化变分模态分解（VMD）与改进阈值降噪的滚动轴承故障特征提取方法。首先，通过鲸鱼优化算法（WOA）优化VMD实现振动信号的自适应分解，建立了L-峭度和相关系数的最优模态分量选取准则；然后对选取的最佳分量进行改进阈值降噪；最后，对降噪后的信号进行希尔伯特包络谱分析实现故障特征频率的提取。在仿真信号和美国西储大学工程数据集上，对提出的方法进行了验证，同时与Teager能量算子降噪的方法和基于包络熵准则的优化方法进行了对比，结果表明提出的方法效果更优。

关键词

特征提取 / 故障诊断 / 鲸鱼优化算法（WOA） / 变分模态分解（VMD） / 阈值降噪

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陈鹏1,赵小强1,2,3. 基于优化VMD与改进阈值降噪的滚动轴承早期故障特征提取[J]. 振动与冲击, 2021, 40(13): 146-153

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