基于MTF的1D-2D-CNN-GRU-Attention时序图像多模态融合的故障识别，适合研究学习（Matlab完整源码和数据），附模型研究报告

基于MTF的1D-2D-CNN-GRU-Attention时序图像多模态融合的故障识别，适合研究学习（Matlab完整源码和数据），附模型研究报告

分类效果

基本描述

摘要：在工业生产和设备运行过程中，滚动轴承故障、变压器油气故障等领域的数据分类与故障诊断至关重要。准确的数据分类与故障诊断能够及时发现设备潜在问题，避免故障恶化导致的生产事故与经济损失。GRU能够捕获时序信息，马尔可夫场（MTF）能够一维信号转换为二维特征图，并结合CNN学习空间特征，MTF-1D-2D-CNN-GRU-Attention模型通过将一维时序信号和二维图像融合，融合不同模态优势，并引入多头自注意力机制提高泛化能力，为数据分类与故障诊断提供了新的思路。实验结果表明，该模型在分类准确率、鲁棒性和泛化能力方面具有显著优势。多模态融合算法凭借其创新点和实验验证的有效性，在滚动轴承故障、变压器油气故障等领域展现出广阔的应用前景，有望推动相关领域故障诊断技术的进一步发展。

关键词：多模态融合；故障诊断；马尔可夫场；卷积神经网络；门控循环单元
适用平台：Matlab2023版本及以上。实验硬件设备配置如下：选用高性能计算机，搭载i7处理器，以确保数据处理和模型训练的高效性；配备16GB的内存，满足大规模数据加载和模型运算过程中的内存需求；使用高性能显卡，提供强大的并行计算能力，加速深度学习模型的训练过程。实验参数的选择依据多方面因素确定。对于卷积神经网络（CNN）部分，卷积核的大小和数量经过多次预实验调优，旨在提取有效特征的同时控制模型复杂度。池化层的步长和池化方式根据数据特点选择，以保留关键信息并降低特征维度。门控循环单元（GRU）的隐藏单元数量通过实验对比不同取值对模型性能的影响来确定，以平衡模型的表达能力和训练难度。学习率的选择参考相关文献经验并结合实际情况微调，以确保模型在训练过程中能够快速收敛且避免陷入局部最优。

程序设计

• 完整代码私信博主回复基于MTF的1D-2D-CNN-GRU-Attention时序图像多模态融合的故障识别，适合研究学习（Matlab完整源码和数据），附模型研究报告

%% 构建序列输入结构
% 故障数值序列
DecMat = SequenceSamples(:,1);
SequenceTrain = arrayDatastore(DecMat,"ReadSize",1,"OutputType","same");%打标签并数据存储
labTrain = TrainLabel';                                                              
LabelTrain = arrayDatastore(labTrain,"ReadSize",1,"OutputType","cell");%% 合并数值存储块
TrainDataStore = combine(imageTtain, SequenceTrain, LabelTrain);%% 测试集处理%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 时序信号处理
TestFeature =  TestData(FeCol, :);                         %% 特征
TestLabel   =  categorical(TestData(LaCol, :));            %% 标签
N = size(TestLabel,2);%% 平铺特征数据
Featuredata  = reshape(TestFeature,1,NumFea,1,N);   %% 时序数据格式转换
NumSample  = N;                                            %% 样本个数
for i=1:NumSampleSequenceSamples{i,1} = Featuredata(:,:,1,i);
end%% 构建图像输入结构
% 指定图像文件夹路径
imageDataFolder = '测试集';% 获取文件夹中的图像文件列表
imageFiles = dir(fullfile(imageDataFolder, '*.png'));      %% 假设图像文件类型为png% 提取文件名（不包含文件扩展名）
fileNames = {imageFiles.name};
[~, fileNamesWithoutExt] = cellfun(@fileparts, fileNames, 'UniformOutput', false);% 将文件名转换为数值
fileIndices = cellfun(@str2double, fileNamesWithoutExt);%% 构建序列输入结构
% 故障数值序列
DecMat = SequenceSamples(:,1);
SequenceTest = arrayDatastore(DecMat,"ReadSize",1,"OutputType","same");%打标签并数据存储
labTest = TestLabel';                                                              
LabelTest = arrayDatastore(labTest,"ReadSize",1,"OutputType","cell");%% 构建 CNN并联GRU 网络
lgraph = layerGraph();% 添加层分支, 将网络分支添加到层次图中。每个分支均为一个线性层组。
tempLayers = [imageInputLayer([227 227 3],"Name","data_1","Normalization","zscore")convolution2dLayer([3 3],64,"Name","conv1_1","BiasLearnRateFactor",0,"Padding",[3 3 3 3],"Stride",[2 2])batchNormalizationLayer("Name","bn_conv1_1")reluLayer("Name","conv1_relu_1")maxPooling2dLayer([3 3],"Name","pool1_1","Padding",[1 1 1 1],"Stride",[2 2])fullyConnectedLayer(128,"Name","fc")tempLayers = [imageInputLayer([1 120],"Name","data","Normalization","zscore")flattenLayer("Name","flatten_1")gruLayer(128,"Name","gru")];
lgraph = addLayers(lgraph,tempLayers);tempLayers = [additionLayer(2,"Name","addition")selfAttentionLayer(2,50,"Name","attention","NumValueChannels",50,"OutputSize",100)

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229