matlab小波分析提取西储大学轴承数据集一种正常信号和九种不同故障信号的特征的代码

下面是一个基于小波分析提取西储大学轴承数据集一种正常信号和九种不同故障信号的特征的MATLAB代码示例：

clear;clc;
% 加载数据集
load('12k_Drive_End_B005_0.mat');
% 选择一个正常信号和一个故障信号
normal_signal = X097_DE_time;
fault_signal = X118_DE_time;
% 小波分解
[C,L] = wavedec(normal_signal,6,'db4');
% 提取小波系数熵特征，共13个特征
for i = 1:7
    coeffs{i} = detcoef(C,L,i);
    features(i) = wentropy(coeffs{i},'shannon');
end
for i = 8:13
    coeffs{i} = appcoef(C,L,'db4',i-7);
    features(i) = wentropy(coeffs{i},'shannon');
end
% 将特征保存到特征向量中
feature_vector(1,:) = features;
% 重复上述步骤提取故障信号的特征
[C,L] = wavedec(fault_signal,6,'db4');
for i = 1:7
    coeffs{i} = detcoef(C,L,i);
    features(i) = wentropy(coeffs{i},'shannon');
end
for i = 8:13
    coeffs{i} = appcoef(C,L,'db4',i-7);
    features(i) = wentropy(coeffs{i},'shannon');
end
feature_vector(2,:) = features;
% 重复上述步骤提取其余8种故障信号的特征，并将特征保存到feature_vector中

注意，这只是一个示例代码，你需要根据数据集的具体情况进行相应的修改。同时，小波分析是一项复杂的技术，需要深入学习和实践才能熟练掌握。