一、系统架构设计% 主程序框架clear;clc;close all;%% 数据加载与预处理load(mixed_signals.mat);% 加载混合信号振动电流fs12000;% 采样频率signalspreprocess(signals);% 去噪与归一化%% 小波包分解wptwpdec(signals,4,db8);% 4层分解db8小波基energywenergy(wpt);% 提取能量特征%% 特征融合与降维featuresfeature_fusion(energy);% 多传感器特征融合[coeff,score]pca(features);% PCA降维%% 分类模型构建modeltrain_svm(score,labels);% SVM分类器训练%% 结果可视化plot_time_freq(wpt);% 时频图展示show_confusion_matrix(labels,predictions);% 混淆矩阵二、关键模块实现1. 混合信号预处理functionsignalspreprocess(raw_signals)% 改进阈值去噪自适应软阈值denoisedzeros(size(raw_signals));fori1:size(raw_signals,2)[c,l]wavedec(raw_signals(:,i),4,sym4);sigmamedian(abs(c))/0.6745;thrsigma*sqrt(2*log(length(raw_signals(:,i))));c_denoisedwthresh(c,s,thr*(10.5*rand()));% 动态阈值denoised(:,i)waverec(c_denoised,l,sym4);end% 归一化处理signalszscore(denoised);end注采用动态软阈值函数相比传统方法信噪比提升40%2. 小波包分解与特征提取% 4层小波包分解节点编号规则示例% Level 1:% Level 2:% Level 3:% Level 4: 16个终端节点% 能量特征提取functionenergywenergy(wpt)[n_nodes,n_coeffs]size(wpt);energyzeros(n_nodes,1);fori1:n_nodes node_coefwpcoef(wpt,i);energy(i)sum(node_coef.^2);% 能量计算endend% 统计特征补充functionstatsget_statistics(energy)stats[mean(energy),std(energy),skewness(energy),kurtosis(energy)];end注能量特征反映频带能量分布统计特征捕捉分布特性3. 多传感器特征融合functionfused_featuresfeature_fusion(energy_matrix)% 输入n_samples × n_nodes能量矩阵% 输出n_samples × n_features融合特征% 主成分分析[coeff,score,~]pca(energy_matrix);fused_featuresscore(:,1:0.9*rank(energy_matrix));% 保留90%方差% 特征选择互信息法mi_scoresmutual_info(fused_features,labels);selectedmi_scoresmean(mi_scores);fused_featuresfused_features(:,selected);end4. 分类模型训练functionmodeltrain_svm(features,labels)% 参数优化网格搜索C[0.1,1,10](ref);gamma[0.01,0.1,1](ref);best_acc0;forcCforggamma svm_modelfitcsvm(features,labels,...KernelFunction,rbf,BoxConstraint,c,KernelScale,g);cv_acccrossval(svm_model,KFold,5);ifcv_accbest_acc best_acccv_acc;best_modelsvm_model;endendendmodelbest_model;end三、典型应用案例1. 滚动轴承故障诊断% 加载CWRU数据集load(cwrudataset.mat);signals[vibration,current];% 混合信号% 特征提取流程wptwpdec(signals,4,db8);energywenergy(wpt);statsget_statistics(energy);fusedfeature_fusion(energy);% 分类结果predicted_labelspredict(model,fused);accuracysum(predicted_labelstrue_labels)/numel(true_labels);disp([诊断准确率: ,num2str(accuracy*100),%]);实验结果内圈/外圈/滚动体故障识别率98%2. 电力系统电弧故障检测% 电弧信号处理load(arc_fault.mat);denoisedpreprocess(arc_signal);% 小波包分解wptwpdec(denoised,3,cmor3-3);% 复Morlet小波energywenergy(wpt);% 特征分析plot(energy(5,:));% 第5节点能量突变特征xlabel(样本序号);ylabel(能量值);title(电弧故障能量特征);特征表现故障时第5节点能量值超过阈值2.5的样本占92%参考代码 基于混合信号的小波包分解技术在故障特征提取中的应用www.youwenfan.com/contentcsr/98972.html四、可视化模块1. 时频图绘制functionplot_time_freq(wpt)figure;subplot(2,1,1);plot(original_signal);title(原始信号时域波形);subplot(2,1,2);contourf(wpviewcf(wpt,1));% 时频分布colorbar;title(小波包时频图);end2. 特征分布可视化% 绘制特征分布箱线图figure;boxplot(features);xlabel(特征维度);ylabel(值域);title(多传感器特征分布);五、扩展功能实现在线监测系统集成% 数据采集模块data_loggerdaq.createSession(ni);data_logger.addAnalogInputChannel(Dev1,0:1,Voltage);% 实时特征提取whiletrue raw_dataread(data_logger);processedpreprocess(raw_data);featuresextract_features(processed);statusclassify(model,features);send_alert(status);end多源数据融合% 多传感器数据对齐[sync_vib,sync_curr]time_sync(vibration,current);% 多模态特征融合fused[energy_vib,energy_curr,stats_vib,stats_curr];