摘要
变分模态分解(Variational Mode Decomposition,VMD)作为一种有效的信号分解工具,已被广泛应用。然而,VMD的分解效果高度依赖于其参数的选择,参数优化对提高信号分解能力至关重要。本研究旨在通过智能优化算法对VMD的关键参数进行优化,并对比不同算法的性能表现。具体而言,本文采用了海洋掠食者算法、小龙虾算法和非洲秃鹰优化算法来优化VMD的参数,并将其应用于机械故障诊断。通过对比实验结果,分析各算法在故障诊断中的应用效果与优劣,为VMD参数优化提供了理论依据和实践指导。
Variational Mode Decomposition(VMD)has been widely used as an effective signal decomposition tool.However,the decomposition effect of VMD is highly dependent on the selection of its parame-ters,and parameter optimization is crucial for improving signal decomposition capability.This pa-per aims to optimize the key parameters of VMD through intelligent optimization algorithms and com-pare the performance of different algorithms.Specifically,this paper uses the Marine Predators Al-gorithm(MPA),Crayfish Algorithm(COA),and African Vulture Optimization Algorithm(AVOA)to op-timize the parameters of VMD and apply them to mechanical fault diagnosis.By comparing experi-mental results,the application effects and advantages and disadvantages of various algorithms in fault diagnosis are analyzed,providing theoretical basis and practical guidance for VMD parameter optimization.
作者
楼志鹏
杨丹丹
陈思源
赵军
孔维宾
邵宇成
Zhipeng Lou;Dandan Yang;Siyuan Chen;Jun Zhao;Weibin Kong;Yucheng Shao(Youpei College,Yancheng Institute of Technology,Yancheng Jiangsu;School of Information Engineering,Yancheng Institute of Technology,Yancheng Jiangsu)
出处
《建模与仿真》
2024年第6期6009-6018,共10页
Modeling and Simulation
基金
大学生创新创业训练计划资助项目(202410305127Y)。
关键词
变分模态分解
参数优化
智能优化算法
故障诊断
Variational Mode Decomposition
Parameter Optimization
Intelligent Optimization Algorithm
Fault Diagnosis
作者简介
通讯作者:孔维宾。
