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);

---

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:num
            switch type
                case 'healthy'
                    signal = generate_healthy_signal();
                case 'crack'
                    signal = generate_crack_signal();
                case 'wear'
                    signal = generate_wear_signal();
                case 'broken'
                    signal = generate_broken_signal();
            end
            data = [data; signal];
            labels = [labels; type];
        end
    end
end

function 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

---

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);

---

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^level
        energy(i) = sum(coeffs{i}.^2);
    end
end

---

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;
   

---

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。