基于参数自寻优变分模态分解的信号降噪方法

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摘要针对滚动轴承故障信号具有非线性、非平稳、噪声强的特点，提出了一种基于参数自寻优变分模态分解的信号降噪方法。以模态复合熵作为适应度函数，采用改进粒子群算法进行VMD参数自适应寻优，确定VMD最优模态数K和二次惩罚因子α；基于最优K和α，对原始信号进行VMD分解，得到K个本征模态函数（IMF）分量；利用相关系数筛选法，进行模态分量的有效模态和含噪模态识别，利用小波阈值去噪方法对含噪模态进行去噪处理；将有效模态与去噪后的模态进行重构，实现信号降噪。分别用滚动轴承故障仿真信号和实验信号进行验证，并与EMD降噪方法进行比较，结果表明该方法可有效提高故障信号的信噪比，降噪效果明显，有利于滚动轴承故障特征的提取。

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	何成兵1
	车其祥1
	徐振华2
	于庆彬3
	董玉亮1
	程睿翔1

关键词 ：变分模态分解, 改进粒子群算法, 参数自寻优, 信号降噪, 滚动轴承

Abstract：Aiming at the characteristics of nonlinear, nonstationary and strong noise of rolling bearing fault signal, a signal denoising method based on parameter self optimizing Variational Modal Decomposition(VMD) was proposed. Taking the modal compound entropy as the fitness function, the improved particle swarm optimization algorithm(IPSO) was used to adaptively optimize the parameters of VMD, and the optimal mode number K and the second penalty factor α of VMD were determined. Based on the optimal K and α，VMD was used to decompose the original signal and obtain K intrinsic mode function (IMF) components. The correlation coefficient screening method was used to identify the effective modes and noisy modes of IMFs, and the wavelet threshold denoising method was used to denoise the noisy modes. The effective mode and the denoised mode were reconstructed to realize signal noise reduction. The feasibility of the proposed method was validated using both the numerical simulation and practical experimental signal of rolling bearing. Moreover，compared with EMD signal denoising method, the proposed method can effectively improve the signal-to-noise ratio of the fault signal, and the noise reduction effect is obvious, which is conducive to the fault feature extraction of the rolling bearing.

Key words： variational modal decomposition(VMD) improved particle swarm optimization(IPSO) parameter self optimizing signal denoising rolling bearing

收稿日期: 2022-11-10 出版日期: 2023-10-15

引用本文:

何成兵1，车其祥1,徐振华2,于庆彬3,董玉亮1,程睿翔1. 基于参数自寻优变分模态分解的信号降噪方法[J]. 振动与冲击, 2023, 42(19): 283-293.
HE Chengbing1, CHE Qixiang1, XU Zhenhua2, YU Qingbin3, DONG Yuliang1, CHENG Ruixiang1. Signal denoising method based on parametric self-optimizing VMD. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(19): 283-293.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I19/283

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