基于SSA优化VMD算法的滚动轴承故障诊断  

Rolling Bearing Fault Diagnosis Based on SSA Optimized VMD Algorithm

作　　者：金志浩[1] 刘庆宝 JIN Zhi-hao;LIU Qing-bao(Shenyang University of Chemical Technology,Shenyang Liaoning 110142,China)

机构地区：[1]沈阳化工大学,辽宁沈阳110142

出　　处：《计算机仿真》2025年第1期485-490,共6页Computer Simulation

基　　金：国家自然科学基金联合基金项目(U1708254);国家自然科学基金青年科学基金项目(11702178)。

摘　　要：针对滚动轴承在强噪声中提取故障信号频率的时候,变分模态分解(VMD)中模态分量k和惩罚因子α难以确定的问题,提出麻雀优化算法(SSA)与变分模态分解相结合的滚动轴承故障诊断方法。首先,SSA以包络熵为适应度函数来自主搜寻最优解,得到最优参数组合[k, α]。其次,利用优化的参数组合对VMD进行故障信号分解,得到若干本征模态分量(IMF)。最后,通过计算模态分量的峭度值和近似熵来筛选出符合需求的分量进行重构,从而得到最优的降噪信号,根据重构信号的包络谱来得出故障频率。仿真和实验结果表明,上述方法可以得出最优的参数组合,并且很准确的提取故障频率,实现滚动轴承的故障诊断。When extracting the frequency of fault signals for rolling bearings in strong noise,it is difficult to determine the modal component k and penalty factor in Variational Modal Decomposition(VMD)α.A rolling bearing fault diagnosis method combining Sparrow Optimization Algorithm(SSA)and Variational Mode Decomposition is proposed for these problems.First,SSA uses the envelope entropy as the fitness function to search for the optimal solution from the main search,and obtains the optimal parameter combination[k,α].Secondly,the optimized parameter combination is used to decompose the fault signal of VMD and obtain several intrinsic mode components(IMFs).Finally,by calculating the kurtosis and approximate entropy of the modal components,the components that meet the requirements are selected for reconstruction,and the optimal denoising signal is obtained.The fault frequency is determined based on the envelope spectrum of the reconstructed signal.The simulation and experimental results show that this method can obtain the optimal parameter combination and accurately extract fault frequency,achieving fault diagnosis of rolling bearings.

关 键 词：滚动轴承 变分模态分解 麻雀搜索算法 峭度值 近似熵 信号重构 

分 类 号：TH133.33[机械工程—机械制造及自动化]