基于智能优化算法的变分模态分解参数优化对比研究

Comparative Study on Parameter Optimization of Variational Mode Decomposition Based on Intelligent Optimization Algorithm

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。

Abstract: 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 parameters, and parameter optimization is crucial for improving signal decomposition capability. This paper aims to optimize the key parameters of VMD through intelligent optimization algorithms and compare the performance of different algorithms. Specifically, this paper uses the Marine Predators Algorithm (MPA), Crayfish Algorithm (COA), and African Vulture Optimization Algorithm (AVOA) to optimize the parameters of VMD and apply them to mechanical fault diagnosis. By comparing experimental 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.