基于SFLA优化变分模态提取的滚动轴承故障诊断

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摘要为解决变分模态提取（variational mode extraction, VME）在分解轴承故障信号过程中近似中心频率和惩罚因子的选择过于依赖专家经验的问题，提出混合蛙跳算法（shuffled frog leaping algorithm, SFLA）与VME相结合的滚动轴承故障诊断方法。首先，为解决单一指标作为目标函数提取特征时信息不全面的问题，结合信息熵（information entropy, IE）、包络谱峭度和相关系数建立新的参数优化指标—KIC；然后，将KIC的极小值作为SFLA的目标函数自适应地选取VME期望模态的中心频率和惩罚因子；最后，通过包络解调分析期望模态进行故障诊断。仿真信号与轴承试验台相关数据集的分析结果表明，所提出的SFLA-VME方法能够准确地提取出期望模态并诊断轴承故障。

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	张怀彬1
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	王衍学1

关键词 ：滚动轴承, 变分模态提取, 混合蛙跳算法, 包络谱峭度, 信息熵

Abstract：In order to solve problems that the selection of approximate center frequency and penalty factor in variational mode extraction (VME) depends too much on experts’ experience, a rolling bearing fault diagnosis method based on shuffled frog leaping algorithm (SFLA) and VME was proposed. Firstly, in order to solve problems of incomplete information when using a single index as an objective function to extract features, a new parameter optimization index-KIC was established with combining information entropy(IE), envelope spectral kurtosis and correlation coefficient. Then, the minimum value of KIC was used as the objective function of SFLA to adaptively select the approximate center frequency and penalty factor expected modes of VME. Finally, expected modes were analyzed with envelope demodulation for fault diagnosis. The analysis results of simulation signals and the related data sets of the bearing test-bed show that the proposed SFLA-VME method can accurately extract the desired mode and diagnosis of the bearing fault.

Key words： rolling bearing variational mode extraction shuffled frog leaping algorithm envelope spectral kurtosis information entropy

收稿日期: 2023-06-29 出版日期: 2024-05-28

引用本文:

张怀彬1，陈志刚1,2，杨远鹏1，王衍学1. 基于SFLA优化变分模态提取的滚动轴承故障诊断[J]. 振动与冲击, 2024, 43(10): 132-139.
ZHANG Huaibin1，CHEN Zhigang1,2，YANG Yuanpeng1，WANG Yanxue1. Fault diagnosis of rolling bearings based on sfla optimized variational mode extraction. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(10): 132-139.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2024/V43/I10/132

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