PSO-VMD-SVD的改进去噪方法研究

针对VMD(Variational Mode Decomposition)在处理信号时,模态个数K和惩罚参数α的选择会影响信号分解的效果,提出一种改进PSO(Particle Swarm Optimization)优化VMD(PSO-VMD)的算法,通过改进阈值相关系数法选择有效模态分量,根据信号分析对高频噪声进行奇异值去噪重构.对仿真信号和管道泄露信号进行实验,通过选择多个K并结合相关系数法选择模态进行重构,比较不同K对应的重构信号与原信号的信噪比(SNR: Signal to Noise Ratio)、相关系数(CC:Coefficient)、平方绝对误差(SAE: Square Absolute Error)、均方误差(MSE: Mean Square Error)证明改进PSO优化VMD的可靠性.将该方法与改进的PSO-VMD结合豪斯特夫距离和奇异值(HD-SVD: Hustoff Distance-Singular Value Decompositio)、互信息和奇异值(MI-SVD: Mutual Information-Singular Value Decompositio)、相关系数和小波变换(CC-WT: Correlation Coefficient-Wavelet Transform)等方法对比,效果更佳.

Research on Improved Method for Denoising of PSO-VMD-SVD

According to VMD(Variational Mode Decomposition) in processing the signal, the selection of mode number K and parameter α will affect the signal decomposition results, an optimized VMD(Variational Mode Decomposition) algorithm improving PSO(Particle Swarm Optimization) (PSO-VMD) was proposed. Based on the analysis of signal, high frequency noise is reconstructed by singular value denoising. Experiments on simulation signal and pipeline leakage signal, by selecting multiple K and combined with proposed the correlation method to select mode and reconstruct signal, compare SNR ( Signal to Noise Ratio ), correlation CC (Coefficient), SAE(Square Absolute Error), MSE(Mean Square Error) of reconstructed signal of different K and original signal, proved the reliability of optimizing VMD by improved PSO. The proposed method is compared with PSO-VMD combined with HD-SVD(Haussteff Distance and Singular Value), MI-SVD(Mutual Information and Singular Values), CC-WT(Correlation Coefficient and Wavelet Transform) and other methods.The proposed denoising method has the best effect.