【信号去噪】基于麻雀搜索算法优化VM实现信号去噪附matlab代码

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1 内容介绍

为准确提取轴承故障特征信息,提出以样本熵为综合目标函数的变分模态分解(variational mode decomposition,VMD)参数优化方法,并改进了诊断流程实现了无需指定参数优化范围的自适应参数优化算法.通过麻雀算法对综合目标函数最小值进行搜索,以确定模态分量个数及惩罚参数的最佳组合.原始故障信号经最佳参数组合下的VMD方法分解为若干个本征模态函数,选择最小综合目标函数值对应的模态分量进行包络解调分析,进而通过模态分量的包络谱判断轴承故障类型.通过实测故障信号分析表明,该方法能够从噪声干扰中有效提取到早期故障信号的微弱故障特征,实现了轴承故障类型的准确判定,验证了该方法的有效性.

2 仿真代码

function SSA_VMD(varargin)

SearchAgents_no = cell2mat(varargin(1));

Max_iteration = cell2mat(varargin(2));

dim = cell2mat(varargin(3));

lb = cell2mat(varargin(4));

ub = cell2mat(varargin(5));

try

imf_write = cell2mat(varargin(6));

catch

imf_write = 0;

end

t = evalin('base', 't');

s = evalin('base', 's');

fs = evalin('base', 'fs');

ST = 0.6;

PD = 0.7;

SD = 0.2;

PDNumber = round(SearchAgents_no * PD);

SDNumber = round(SearchAgents_no - SearchAgents_no * SD);

if (max(size(ub)) == 1)

ub = ub .* ones(1, dim);

lb = lb .* ones(1, dim);

end

X0 = initialization(SearchAgents_no, dim, ub, lb);

X = X0;

fitness = zeros(1, SearchAgents_no);

for i = 1:SearchAgents_no

fitness(i) = fun(X(i, :), s);

end

[fitness, index] = sort(fitness);

BestF = fitness(1);

WorstF = fitness(end);

GBestF = fitness(1);

for i = 1:SearchAgents_no

X(i, :) = X0(index(i), :);

end

Convergence_curve = zeros(Max_iteration, 1);

GBestX = X(1, :);

X_new = X;

gbest_array = zeros(Max_iteration, 2);

for i = 1:Max_iteration

BestF = fitness(1);

WorstF = fitness(end);

R2 = rand(1);

for j = 1:PDNumber

if (R2 < ST)

X_new(j, :) = X(j, :) .* exp(- j / (rand(1) * Max_iteration));

else

X_new(j, :) = X(j, :) + randn() * ones(1, dim);

end

for j = PDNumber + 1:SearchAgents_no

if (j > (SearchAgents_no - PDNumber) / 2 + PDNumber)

X_new(j, :) = randn() .* exp((X(end, :) - X(j, :)) / j^2);

else

A = ones(1, dim);

for a = 1:dim

if (rand() > 0.5)

A(a) =- 1;

end

AA = A' * inv(A * A');

X_new(j, :) = X(1, :) + abs(X(j, :) - X(1, :)) .* AA';

end

Temp = randperm(SearchAgents_no);

SDchooseIndex = Temp(1:SDNumber);

for j = 1:SDNumber

if (fitness(SDchooseIndex(j)) > BestF)

X_new(SDchooseIndex(j), :) = X(1, :) + randn() .* abs(X(SDchooseIndex(j), :) - X(1, :));

elseif (fitness(SDchooseIndex(j)) == BestF)

K = 2 * rand() - 1;

X_new(SDchooseIndex(j), :) = X(SDchooseIndex(j), :) + K .* (abs(X(SDchooseIndex(j), :) - X(end, :)) ./ (fitness(SDchooseIndex(j)) - fitness(end) + 10^ - 8));

end

for j = 1:SearchAgents_no

for a = 1:dim

if (X_new(j, a) > ub(a))

X_new(j, a) = ub(a);

end

if (X_new(j, a) < lb(a))

X_new(j, a) = lb(a);

end

for j = 1:SearchAgents_no

fitness_new(j) = fun(X_new(j, :), s);

end

for j = 1:SearchAgents_no

if (fitness_new(j) < GBestF)

GBestF = fitness_new(j);

GBestX = X_new(j, :);

end

X = X_new;

fitness = fitness_new;

[fitness, index] = sort(fitness);

BestF = fitness(1);

WorstF = fitness(end);

for j = 1:SearchAgents_no

X(j, :) = X(index(j), :);

end

disp(['current iteration is: ', num2str(i), ', best fitness is: ', num2str(GBestF)])

gbest_array(i, :) = GBestX;

Convergence_curve(i) = GBestF;

end

Best_pos = GBestX;

flag = 1;

figure(1)

plot(Convergence_curve, 'k-.o', 'linewidth', 1)

legend('最佳适应度')

grid on

xlabel('进化代数')

ylabel('包络熵')

title('SSA进化曲线')

figure(2)

plot(gbest_array(:, 1), 'ks-.', 'linewidth', 1)

grid on

xlabel('进化代数')

ylabel('惩罚因子')

title('惩罚因子的优化过程曲线')

figure(3)

plot(round(gbest_array(:, 2)), 'kv-.', 'linewidth', 1)

grid on

xlabel('进化代数')

ylabel('分解的模态数')

title('分解模态数的优化过程曲线')

alpha = Best_pos(1);

K = round(Best_pos(2));

tau = 0;

DC = 0;

init = 1;

tol = 1e-7;

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

[m, ~] = size(u);

m = m + 1;

figure(4)

for i = 1:m - 1

subplot(m, 1, i)

plot(t, u(i, :), 'b-', 'linewidth', 1)

ylabel(['IMF', num2str(i)]);

end

res = s' - sum(u, 1);

subplot(m, 1, m)

plot(t, res, 'b-', 'linewidth', 1)

ylabel('Res');

xlabel('t/s')

figure(5)

for i = 1:m - 1

subplot(m, 1, i)

[cc, y_f] = plot_fft(u(i, :), fs, 1);

plot(y_f, cc, 'b', 'LineWIdth', 1);

ylabel(['FFT of IMF', num2str(i)]);

end

subplot(m, 1, m)

[cc, y_f] = plot_fft(res, fs, 1);

plot(y_f, cc, 'b', 'LineWIdth', 1);

ylabel('FFT of Res');

xlabel('f/Hz')

fig_text(flag)

% figure(6)

% imshow(a. personal_id);

% title( 'Color', 'red', 'FontSize', 16,...

% 'HorizontalAlignment', 'center',...

% 'Interpreter', 'none')

% fprintf_result(Best_pos, s);

% if imf_write == 1

% xlswrite('imf结果.xlsx', u', 'u', 'A1');

% xlswrite('imf结果.xlsx', u_hat, 'uhat', 'A1');

% xlswrite('imf结果.xlsx', omega, 'omega', 'A1');

% end

3 运行结果

4 参考文献

[1]胡鸿志, 覃畅, 管芳,等. 基于麻雀搜索算法优化支持向量机的刀具磨损识别[J]. 科学技术与工程, 2021, 21(25):7.

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

部分理论引用网络文献，若有侵权联系博主删除。

【信号去噪】基于麻雀搜索算法优化VM实现信号去噪附matlab代码

1 内容介绍

2 仿真代码

3 运行结果

​4 参考文献

博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。

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4 参考文献