基于MATLAB的齿轮故障检测

一、系统架构设计

%% 主程序框架
clear; clc; close all;%% 参数设置
fs = 10000;       % 采样频率
t = 0:1/fs:1;     % 时间向量
fault_types = {'healthy','crack','wear','broken'};
num_samples = 10; % 每类样本数量%% 数据生成与加载
[data, labels] = generate_fault_data(fault_types, num_samples);%% 信号预处理
denoised_data = preprocess(data);%% 时域分析
time_features = extract_time_features(denoised_data);%% 频域分析
freq_features = extract_freq_features(denoised_data, fs);%% 特征融合与分类
diagnosis_result = diagnose(time_features, freq_features);%% 可视化
plot_diagnosis_results(diagnosis_result);

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二、核心模块实现

2.1 数据生成模块

function [data, labels] = generate_fault_data(types, num)% 生成仿真故障数据data = [];labels = [];for i = 1:numel(types)type = types{i};for j = 1:numswitch typecase 'healthy'signal = generate_healthy_signal();case 'crack'signal = generate_crack_signal();case 'wear'signal = generate_wear_signal();case 'broken'signal = generate_broken_signal();enddata = [data; signal];labels = [labels; type];endend
endfunction signal = generate_healthy_signal()% 健康齿轮振动信号f_mesh = 500; % 啮合频率t = 0:1/fs:1;signal = sin(2*pi*f_mesh*t) + 0.2*randn(size(t));
end

2.2 时域分析模块

function features = extract_time_features(signal)% 时域特征提取features.RMS = rms(signal);features.Var = var(signal);features.Skewness = skewness(signal);features.Kurtosis = kurtosis(signal);features.Impulse = sum(abs(signal(2:end)-signal(1:end-1)));
end

2.3 频域分析模块

function features = extract_freq_features(signal, fs)% 频域特征提取N = length(signal);Y = fft(signal);P2 = abs(Y/N);P1 = P2(1:N/2+1);P1(2:end-1) = 2*P1(2:end-1);freq = fs*(0:N/2)/N;% 关键频率成分提取[peak_freq, peak_amp] = findpeaks(P1, 'SortStr', 'descend');features.MainFreq = peak_freq(1);features.SecondFreq = peak_freq(2);features.PeakRatio = peak_amp(2)/peak_amp(1);% 齿轮特征频率计算z1 = 20; z2 = 50; % 齿数f_mesh = (z2/(z1+z2)) * (fs/60); % 理论啮合频率features.MeshFreq = f_mesh;
end

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三、故障诊断算法

3.1 基于SVM的分类器

function result = diagnose(time_feat, freq_feat)% 加载预训练模型load('svm_model.mat'); % 包含训练好的分类器% 特征融合combined_feat = [time_feat, freq_feat];% 预测predicted_label = predict(svm_model, combined_feat);result = predicted_label;
end

3.2 深度学习增强方案

layers = [ ...sequenceInputLayer(6) % 输入层（6维特征）lstmLayer(20)         % LSTM层fullyConnectedLayer(4) % 输出层（4类故障）classificationLayer];options = trainingOptions('adam',...'MaxEpochs',50,...'MiniBatchSize',32);net = trainNetwork(XTrain,YTrain,layers,options);

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四、关键分析技术

4.1 包络分析（冲击检测）

function envelope = envelope_analysis(signal)% 包络线提取[b,a] = butter(4,0.1);filtered = filter(b,a,signal);envelope = abs(hilbert(filtered));
end

4.2 小波包分解

function coeffs = wavelet_analysis(signal)% 小波包分解w = 'db4';level = 4;coeffs = wpdec(signal,level,w);% 能量计算energy = zeros(1,2^level);for i = 1:2^levelenergy(i) = sum(coeffs{i}.^2);end
end

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五、结果可视化

function plot_diagnosis_results(result)% 混淆矩阵cm = confusionmat(true_labels, result);figure;confusionchart(cm);title('混淆矩阵');% 特征分布对比figure;subplot(2,2,1);boxplot([time_feat.healthy; time_feat.crack], 'Labels',{'Healthy','Crack'});title('RMS分布');subplot(2,2,2);histogram(freq_feat.MainFreq, 'Normalization','pdf');hold on;line([mesh_freq,mesh_freq], ylim, 'r--');title('啮合频率分布');
end

参考代码 Matlab用于齿轮故障检测 www.youwenfan.com/contentcnj/64408.html

六、性能优化

1. 并行计算加速

   % 使用parfor加速特征提取
   parfor i = 1:numel(data)features(i) = extract_features(data(i));
   end
   

2. GPU加速方案

   % 将数据转移至GPU
   gpu_data = gpuArray(data);% 使用cuDNN加速卷积网络
   net = trainNetwork(XTrain,YTrain,layers,options,'ExecutionEnvironment','multi-gpu');
   

3. 实时诊断优化

   % 滑动窗口处理
   window_size = 0.1; % 100ms窗口
   overlap = 0.05;    % 50%重叠
   num_windows = floor((length(signal)-window_size)/overlap) +1;
   

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该方法通过融合时域统计特征与频域频谱特征，结合机器学习算法，实现了对齿轮早期故障的有效检测。实际应用中建议根据具体设备参数调整特征提取算法，并通过实验验证优化模型参数。