The vibration signal of rolling bearing is complex, it is difficult to extract fault features and diagnose accurately. In this paper, a rolling bearing fault diagnosis method based on variational mode decomposition and GWO-LSSVM is proposed. The variational mode decomposition algorithm is used to decompose the bearing vibration signal, and the fuzzy entropy of each component signal is calculated. GWO is used to optimize the parameters of LSSVM. The least square support vector machine is used to identify the bearing fault.

中文翻译：

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基于VMD和LSSVM的滚动轴承故障诊断方法

滚动轴承的振动信号复杂，难以准确提取故障特征和诊断。本文提出了一种基于变分模态分解和GWO-LSSVM的滚动轴承故障诊断方法。采用变分模态分解算法对轴承振动信号进行分解，计算各分量信号的模糊熵。GWO 用于优化 LSSVM 的参数。最小二乘支持向量机用于识别轴承故障。