基于resnet的轴承故障检测matlab代码

基于ResNet的轴承故障检测的Matlab代码如下所示[^1]：

% 导入数据
load('bearing_dataset.mat');

% 数据预处理
X = permute(data, [2, 3, 4, 1]); % 调整数据维度
Y = categorical(label); % 转换标签为分类变量

% 划分训练集和测试集
[trainInd, valInd, testInd] = dividerand(size(X, 4), 0.7, 0.15, 0.15);
XTrain = X(:, :, :, trainInd);
YTrain = Y(trainInd);
XVal = X(:, :, :, valInd);
YVal = Y(valInd);
XTest = X(:, :, :, testInd);
YTest = Y(testInd);

% 构建ResNet网络
layers = [
    imageInputLayer([1000 1 1])
    convolution2dLayer(100, 1, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(200, 1, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    convolution2dLayer(400, 1, 'Padding', 'same')
    batchNormalizationLayer
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)
    fullyConnectedLayer(4)
    softmaxLayer
    classificationLayer];

% 设置训练选项
options = trainingOptions('adam', ...
    'MiniBatchSize', 32, ...
    'MaxEpochs', 20, ...
    'ValidationData', {XVal, YVal}, ...
    'ValidationFrequency', 10, ...
    'Verbose', false, ...
    'Plots', 'training-progress');

% 训练网络
net = trainNetwork(XTrain, YTrain, layers, options);

% 在测试集上评估网络性能
YTestPred = classify(net, XTest);
accuracy = sum(YTestPred == YTest) / numel(YTest);

% 显示准确率
disp(['测试集准确率：', num2str(accuracy)]);

请注意，上述代码仅为示例，实际使用时需要根据数据集的特点进行相应的调整和优化。