故障诊断时域、频域及时频域特征提取matlab代码

clc % 清空命令行 clear all % 清空变量 close all % 关闭开启的图窗 filename = 'C:\Users\Desktop\Feature extraction\feature.xlsx'; % 输入文件的绝对路径 [num,txt,raw] = xlsread(filename,'datasets','A1:CV1596'); % 读取Excel文件中的数据 features3 = table; % 新建时频域特征表 for i = 1:1596 % 故障样本数量 v = num(i,:); % 提取第i行所有的数量 n=3; % 3层小波包分解 T=wpdec(v,n,'db6'); % 3层小波包分解,得到8个子带 for j=1:2^n E(j)=norm(wprcoef(T,[n,j-1]),2)*norm(wprcoef(T,[n,j-1]),2); % 求第j个节点的二范数，乘以平方才是能量 end E_total=sum(E); features3.p1(i)=E(1)/E_total; % 8个子带的小波能量比 features3.p2(i)=E(2)/E_total; features3.p3(i)=E(3)/E_total; features3.p4(i)=E(4)/E_total; features3.p5(i)=E(5)/E_total; features3.p6(i)=E(6)/E_total; features3.p7(i)=E(7)/E_total; features3.p8(i)=E(8)/E_total; features3.WEEntropy(i)=-sum(E/E_total.*log(E/E_total)); % 1、小波能量熵(小波能谱熵)(Wavelet Energy Entropy) % 2、8个子带的小波熵(小波尺度熵，Wavelet Scale Entropy)，在各尺度上求的是shannon熵=小波尺度熵(小波特征尺度熵) features3.E1(i)=wentropy(wpcoef(T,[3,0]),'shannon'); features3.E2(i)=wentropy(wpcoef(T,[3,1]),'shannon'); features3.E3(i)=wentropy(wpcoef(T,[3,2]),'shannon'); features3.E4(i)=wentropy(wpcoef(T,[3,3]),'shannon'); features3.E5(i)=wentropy(wpcoef(T,[3,4]),'shannon'); features3.E6(i)=wentropy(wpcoef(T,[3,5]),'shannon'); features3.E7(i)=wentropy(wpcoef(T,[3,6]),'shannon'); features3.E8(i)=wentropy(wpcoef(T,[3,7]),'shannon'); % 3、小波奇异熵(Wavelet Singular Entropy) % 先求取奇异值，将奇异值构造向量即得到奇异值谱，然后计算熵值即可 % 小波分解，得到各分量信号，对系数重构，得到完整系数 for j=1:2^n % 重构分量，使信号和原始信号一样长 q(:,j)=wprcoef(T,[n,j-1]); % 求系数 end s=svd(q); % 奇异值分解 S=sum(s); dim=length(s); for j=1:dim p(j)=s(j)/S; end features3.WSEntropy(i)=-sum(p.*log(p)); end writetable(features3,'C:\Users\Desktop\Feature extraction\feature.xlsx','WriteVariableNames',true,'Sheet','WavFeatures','Range','A1');