matlab西储大学轴承数据集一维cnn代码轴承故障诊断
时间: 2023-09-01 21:07:57 浏览: 123
以下是一维CNN在Matlab中进行轴承故障诊断的代码示例:
1. 加载数据集和必要的工具包
```matlab
load('data_200hz.mat'); % 加载数据集
addpath('Deep Learning Toolbox');
```
2. 数据预处理
```matlab
X = data'; % 将数据集转换为列向量
Y = categorical(repmat([1 2 3 4],1,10)); % 为每个类别(正常、内圈故障、外圈故障、滚动体故障)分配类别标签
Y = Y';
% 将数据集分为训练集和测试集
[trainInd,testInd] = dividerand(length(Y),0.7,0.3);
XTrain = X(trainInd,:);
YTrain = Y(trainInd,:);
XTest = X(testInd,:);
YTest = Y(testInd,:);
```
3. 构建CNN模型
```matlab
inputSize = 512; % 输入信号长度
numFilters = 32; % 卷积层滤波器数量
filterSize = 20; % 卷积层滤波器长度
numClasses = 4; % 类别数量
layers = [
sequenceInputLayer(inputSize)
convolution1dLayer(filterSize,numFilters,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
convolution1dLayer(filterSize,numFilters,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
convolution1dLayer(filterSize,numFilters,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
convolution1dLayer(filterSize,numFilters,'Padding','same')
batchNormalizationLayer
reluLayer
maxPooling1dLayer(2,'Stride',2)
fullyConnectedLayer(numClasses)
softmaxLayer
classificationLayer];
options = trainingOptions('sgdm', ...
'InitialLearnRate',0.01, ...
'MaxEpochs',50, ...
'MiniBatchSize',10, ...
'Shuffle','every-epoch', ...
'ValidationData',{XTest,YTest}, ...
'ValidationFrequency',50, ...
'Verbose',false, ...
'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options); % 训练模型
```
4. 模型测试
```matlab
YPred = classify(net,XTest); % 对测试数据进行分类
accuracy = sum(YPred == YTest)/numel(YTest); % 计算分类准确率
```
这是一个基本的一维CNN模型代码示例,你可以根据自己的需要进行修改和优化。