【特征提取】基于麻雀算法优化VMD实现西储大学数据集故障信号特征提取附matlab代码

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智能优化算法       神经网络预测       雷达通信       无线传感器        电力系统

信号处理              图像处理               路径规划       元胞自动机        无人机 

⛄ 内容介绍

一种基于SSAVMD算法的信号去噪方法,具体为:根据麻雀优化算法数学模型,然后进行初始化参数,在取值范围内初始化麻雀的位置向量,根据位置向量对原始振动信号进行VMD分解,然后计算每个麻雀位置下的平均包络熵;更新最小平均包络熵,即获得当前群体中最佳个体的位置更新当前麻雀个体的空间位置;输出最佳麻雀个体的位置向量,即得VMD的分解参数组合;根据所得到的分解参数组合对信号进行VMD分解,将分解出IMF分量相加得到重构信号,即得去噪后的信号.本发明解决了现有技术中存在的原始VMD算法的分解参数需要根据经验进行人工确定,导致无法得到最优分解结果,从而影响信号的去噪效果的问题.

⛄ 部分代码

function [u, u_hat, omega] = VMD(signal, alpha, tau, K, DC, init, tol)

% Variational Mode Decomposition

% Authors: Konstantin Dragomiretskiy and Dominique Zosso

% [email protected] --- http://www.math.ucla.edu/~zosso

% Initial release 2013-12-12 (c) 2013

%

% Input and Parameters:

% ---------------------

% signal  - the time domain signal (1D) to be decomposed

% alpha   - the balancing parameter of the data-fidelity constraint

% tau     - time-step of the dual ascent ( pick 0 for noise-slack )

% K       - the number of modes to be recovered

% DC      - true if the first mode is put and kept at DC (0-freq)

% init    - 0 = all omegas start at 0

%                    1 = all omegas start uniformly distributed

%                    2 = all omegas initialized randomly

% tol     - tolerance of convergence criterion; typically around 1e-6

%

% Output:

% -------

% u       - the collection of decomposed modes

% u_hat   - spectra of the modes

% omega   - estimated mode center-frequencies

%

% When using this code, please do cite our paper:

% -----------------------------------------------

% K. Dragomiretskiy, D. Zosso, Variational Mode Decomposition, IEEE Trans.

% on Signal Processing (in press)

% please check here for update reference: 

%          http://dx.doi.org/10.1109/TSP.2013.2288675

%---------- Preparations

% Period and sampling frequency of input signal

save_T = length(signal);

fs = 1/save_T;

% extend the signal by mirroring

T = save_T;

f_mirror(1:T/2) = signal(T/2:-1:1);

f_mirror(T/2+1:3*T/2) = signal;

f_mirror(3*T/2+1:2*T) = signal(T:-1:T/2+1);

f = f_mirror;

% Time Domain 0 to T (of mirrored signal)

T = length(f);

t = (1:T)/T;

% Spectral Domain discretization

freqs = t-0.5-1/T;

% Maximum number of iterations (if not converged yet, then it won't anyway)

N = 500;

% For future generalizations: individual alpha for each mode

Alpha = alpha*ones(1,K);

% Construct and center f_hat

f_hat = fftshift((fft(f)));

f_hat_plus = f_hat;

f_hat_plus(1:T/2) = 0;

% matrix keeping track of every iterant // could be discarded for mem

u_hat_plus = zeros(N, length(freqs), K);

% Initialization of omega_k

omega_plus = zeros(N, K);

switch init

    case 1

        for i = 1:K

            omega_plus(1,i) = (0.5/K)*(i-1);

        end

    case 2

        omega_plus(1,:) = sort(exp(log(fs) + (log(0.5)-log(fs))*rand(1,K)));

    otherwise

        omega_plus(1,:) = 0;

end

% if DC mode imposed, set its omega to 0

if DC

    omega_plus(1,1) = 0;

end

% start with empty dual variables

lambda_hat = zeros(N, length(freqs));

% other inits

uDiff = tol+eps; % update step

n = 1; % loop counter

sum_uk = 0; % accumulator

% ----------- Main loop for iterative updates

while ( uDiff > tol &&  n < N ) % not converged and below iterations limit

    

    % update first mode accumulator

    k = 1;

    sum_uk = u_hat_plus(n,:,K) + sum_uk - u_hat_plus(n,:,1);

    

    % update spectrum of first mode through Wiener filter of residuals

    u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2);

    

    % update first omega if not held at 0

    if ~DC

        omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2);

    end

    

    % update of any other mode

    for k=2:K

        

        % accumulator

        sum_uk = u_hat_plus(n+1,:,k-1) + sum_uk - u_hat_plus(n,:,k);

        

        % mode spectrum

        u_hat_plus(n+1,:,k) = (f_hat_plus - sum_uk - lambda_hat(n,:)/2)./(1+Alpha(1,k)*(freqs - omega_plus(n,k)).^2);

        

        % center frequencies

        omega_plus(n+1,k) = (freqs(T/2+1:T)*(abs(u_hat_plus(n+1, T/2+1:T, k)).^2)')/sum(abs(u_hat_plus(n+1,T/2+1:T,k)).^2);

        

    end

    

    % Dual ascent

    lambda_hat(n+1,:) = lambda_hat(n,:) + tau*(sum(u_hat_plus(n+1,:,:),3) - f_hat_plus);

    

    % loop counter

    n = n+1;

    

    % converged yet?

    uDiff = eps;

    for i=1:K

        uDiff = uDiff + 1/T*(u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i))*conj((u_hat_plus(n,:,i)-u_hat_plus(n-1,:,i)))';

    end

    uDiff = abs(uDiff);

    

end

%------ Postprocessing and cleanup

% discard empty space if converged early

N = min(N,n);

omega = omega_plus(1:N,:);

% Signal reconstruction

u_hat = zeros(T, K);

u_hat((T/2+1):T,:) = squeeze(u_hat_plus(N,(T/2+1):T,:));

u_hat((T/2+1):-1:2,:) = squeeze(conj(u_hat_plus(N,(T/2+1):T,:)));

u_hat(1,:) = conj(u_hat(end,:));

u = zeros(K,length(t));

for k = 1:K

    u(k,:)=real(ifft(ifftshift(u_hat(:,k))));

end

% remove mirror part

u = u(:,T/4+1:3*T/4);

% recompute spectrum

clear u_hat;

for k = 1:K

    u_hat(:,k)=fftshift(fft(u(k,:)))';

end

end

⛄ 运行结果

⛄ 参考文献

[1]孟博, 余中舟, 郇战,等. 基于VMD-SSA-LSSVM的齿轮箱故障检测模型:, CN114964783A[P]. 2022.

[2]逯全波. 基于改进VMD的滚动轴承故障无线监测系统[D]. 重庆三峡学院.

⛳️ 完整代码

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