JJCR一区 | Matlab实现GAF-PCNN、GASF-CNN、GADF-CNN的多特征输入数据分类预测/故障诊断

分类效果

格拉姆矩阵图

GAF-PCNN

GASF-CNN

GADF-CNN

基本介绍

1.Matlab实现GAF-PCNN、GASF-CNN、GADF-CNN的多特征输入数据分类预测/故障诊断，三个模型对比，运行环境matlab2023b；

2.先运行格拉姆矩阵变换进行数据转换，然后运行分别GAF_PCNN.mGADF_CNN.m，GASF_CNN.m完成多特征输入数据分类预测/故障诊断；

GADF_CNN.m，是只用到了格拉姆矩阵的GADF矩阵，将GADF矩阵送入CNN进行故障诊断。

GASF_CNN.m，是只用到了格拉姆矩阵的GASF矩阵，将GASF矩阵送入CNN进行故障诊断。

GAF_PCNN.m，是将GASF 图与GADF 图同时送入两条并行CNN 中，经过卷积-池化后，两条CNN网络各输出一组一维向量；然后，将所输出两组一维向量进行拼接融合；通过全连接层后，最终将融合特征送入到Softmax 分类器中。

参考文献

• PCNN结构
  
• CNN结构
  

程序设计

• 完整程序和数据获取方式私信博主回复Matlab实现GAF-PCNN、GASF-CNN、GADF-CNN的多特征输入数据分类预测/故障诊断。

    fullyConnectedLayer(classnum,'Name','fc12')
    softmaxLayer('Name','softmax')
    classificationLayer('Name','classOutput')];

lgraph = layerGraph(layers1);

layers2 = [imageInputLayer([size(input2,1) size(input2,2)],'Name','vinput')  
    
    flattenLayer(Name='flatten2')
    
    bilstmLayer(15,'Outputmode','last','name','bilstm') 
    dropoutLayer(0.1)        % Dropout层，以概率为0.2丢弃输入
    reluLayer('Name','relu_2')
    selfAttentionLayer(2,2,"Name","mutilhead-attention")   %Attention机制
    fullyConnectedLayer(10,'Name','fc21')];
lgraph = addLayers(lgraph,layers2);
lgraph = connectLayers(lgraph,'fc21','add/in2');

plot(lgraph)


%% Set the hyper parameters for unet training
options = trainingOptions('adam', ...                 % 优化算法Adam
    'MaxEpochs', 1000, ...                            % 最大训练次数
    'GradientThreshold', 1, ...                       % 梯度阈值
    'InitialLearnRate', 0.001, ...         % 初始学习率
    'LearnRateSchedule', 'piecewise', ...             % 学习率调整
    'LearnRateDropPeriod',700, ...                   % 训练100次后开始调整学习率
    'LearnRateDropFactor',0.01, ...                    % 学习率调整因子
    'L2Regularization', 0.001, ...         % 正则化参数
    'ExecutionEnvironment', 'cpu',...                 % 训练环境
    'Verbose', 1, ...                                 % 关闭优化过程
    'Plots', 'none');                    % 画出曲线
%Code introduction
if nargin<2
    error('You have to supply all required input paremeters, which are ActualLabel, PredictedLabel')
end
if nargin < 3
    isPlot = true;
end

%plotting the widest polygon
A1=1;
A2=1;
A3=1;
A4=1;
A5=1;
A6=1;

a=[-A1 -A2/2 A3/2 A4 A5/2 -A6/2 -A1];
b=[0 -(A2*sqrt(3))/2 -(A3*sqrt(3))/2 0 (A5*sqrt(3))/2 (A6*sqrt(3))/2 0];

if isPlot
    figure   
    plot(a, b, '--bo','LineWidth',1.3)
    axis([-1.5 1.5 -1.5 1.5]);
    set(gca,'FontName','Times New Roman','FontSize',12);
    hold on
    %grid
end


% Calculating the True positive (TP), False Negative (FN), False Positive...
% (FP),True Negative (TN), Classification Accuracy (CA), Sensitivity (SE), Specificity (SP),...
% Kappa (K) and F  measure (F_M) metrics
PositiveClass=max(ActualLabel);
NegativeClass=min(ActualLabel);
cp=classperf(ActualLabel,PredictedLabel,'Positive',PositiveClass,'Negative',NegativeClass);
 CM=cp.DiagnosticTable;
    TP=CM(1,1);
    FN=CM(2,1);
    FP=CM(1,2);
    TN=CM(2,2);
    CA=cp.CorrectRate;
    SE=cp.Sensitivity; %TP/(TP+FN)
    SP=cp.Specificity; %TN/(TN+FP)
    Pr=TP/(TP+FP);
    Re=TP/(TP+FN);
    F_M=2*Pr*Re/(Pr+Re);
    FPR=FP/(TN+FP);
    TPR=TP/(TP+FN);
    K=TP/(TP+FP+FN);
    [X1,Y1,T1,AUC] = perfcurve(ActualLabel,PredictedLabel,PositiveClass); 
    %ActualLabel(1) means that the first class is assigned as positive class
    %plotting the calculated CA, SE, SP, AUC, K and F_M on polygon
x=[-CA -SE/2 SP/2 AUC K/2 -F_M/2 -CA];
y=[0 -(SE*sqrt(3))/2 -(SP*sqrt(3))/2 0 (K*sqrt(3))/2 (F_M*sqrt(3))/2 0];

if isPlot
    plot(x, y, '-ko','LineWidth',1)
    set(gca,'FontName','Times New Roman','FontSize',12);
%     shadowFill(x,y,pi/4,80)
    fill(x, y,[0.8706 0.9216 0.9804])
end

%calculating the PAM value
% Get the number of vertices
n = length(x);
% Initialize the area
p_area = 0;
% Apply the formula
for i = 1 : n-1
    p_area = p_area + (x(i) + x(i+1)) * (y(i) - y(i+1));
end
p_area = abs(p_area)/2;

%Normalization of the polygon area to one.
PA=p_area/2.59807;

if isPlot
    %Plotting the Polygon
    plot(0,0,'r+')
    plot([0 -A1],[0 0] ,'--ko')
    text(-A1-0.3, 0,'CA','FontWeight','bold','FontName','Times New Roman')
    plot([0 -A2/2],[0 -(A2*sqrt(3))/2] ,'--ko')
    text(-0.59,-1.05,'SE','FontWeight','bold','FontName','Times New Roman')
    plot([0 A3/2],[0 -(A3*sqrt(3))/2] ,'--ko')
    text(0.5, -1.05,'SP','FontWeight','bold','FontName','Times New Roman')
    plot([0 A4],[0 0] ,'--ko')
    text(A4+0.08, 0,'AUC','FontWeight','bold','FontName','Times New Roman')
    plot([0 A5/2],[0 (A5*sqrt(3))/2] ,'--ko')
    text(0.5, 1.05,'J','FontWeight','bold','FontName','Times New Roman')

    daspect([1 1 1])
end
Metrics.PA=PA;
Metrics.CA=CA;
Metrics.SE=SE;
Metrics.SP=SP;
Metrics.AUC=AUC;
Metrics.K=K;
Metrics.F_M=F_M;


printVar(:,1)=categories;
printVar(:,2)={PA, CA, SE, SP, AUC, K, F_M};
disp('预测结果打印:')
for i=1:length(categories)
    fprintf('%23s: %.2f \n', printVar{i,1}, printVar{i,2})
end

参考资料

[1] https://blog.csdn.net/kjm13182345320/category_11799242.html?spm=1001.2014.3001.5482
[2] https://blog.csdn.net/kjm13182345320/article/details/124571691