基于改进变模分解的调制信号去噪方法研究

CanYu Mo, Qianqiang Lin, Yuanduo Niu, Haoran Du
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引用次数: 0

摘要

为了进一步分析旋转部件产生的微动调制信号并提取微动特征,提出了一种基于改进的变模分解(VMD)的调制信号去噪算法。为了提高时频性能,该方法将数据分解为窄带信号,并分析信号内部的能量和频率变化。遗传算法用于自适应优化 VMD 过程中的模式数和带宽控制参数。这种方法旨在获得最佳参数组合,并对微动调制信号进行模式分解。确定了 VMD 的最佳模式数和二次惩罚因子。根据模式数和二次惩罚因子的最佳值,使用 VMD 对原始信号进行分解,得到最佳模式数的本征模式函数(IMF)分量。然后用去噪模式重建有效模式,实现信号去噪。通过实验数据验证,所提出的算法对调制信号进行了有效去噪。在仿真数据验证中,该算法实现了最高的信噪比(SNR),表现出最佳性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Modulation Signal Denoising Method Based on Improved Variational Mode Decomposition
In order to further analyze the micro-motion modulation signals generated by rotating components and extractmicro-motion features, a modulation signal denoising algorithm based on improved variational mode decomposition (VMD)is proposed. To improve the time-frequency performance, this method decomposes the data into narrowband signalsand analyzes the internal energy and frequency variations within the signal. Genetic algorithms are used to adaptivelyoptimize the mode number and bandwidth control parameters in the process of VMD. This approach aims to obtain theoptimal parameter combination and perform mode decomposition on the micro-motion modulation signal. The optimalmode number and quadratic penalty factor for VMD are determined. Based on the optimal values of the mode numberand quadratic penalty factor, the original signal is decomposed using VMD, resulting in optimal mode number intrinsicmode function (IMF) components. The effective modes are then reconstructed with the denoised modes, achieving signaldenoising. Through experimental data verification, the proposed algorithm demonstrates effective denoising of modulationsignals. In simulation data validation, the algorithm achieves the highest signal-to-noise ratio (SNR) and exhibits the bestperformance.
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