基于MTF的1D-2D-CNN-GRU-Attention時序圖像多模態融合的故障識別，適合研究學習（Matlab完整源碼和數據），附模型研究報告

分類效果

基本描述

摘要：在工業生產和設備運行過程中，滾動軸承故障、變壓器油氣故障等領域的數據分類與故障診斷至關重要。準確的數據分類與故障診斷能夠及時發現設備潛在問題，避免故障惡化導致的生產事故與經濟損失。GRU能夠捕獲時序信息，馬爾可夫場（MTF）能夠一維信號轉換為二維特征圖，并結合CNN學習空間特征，MTF-1D-2D-CNN-GRU-Attention模型通過將一維時序信號和二維圖像融合，融合不同模態優勢，并引入多頭自注意力機制提高泛化能力，為數據分類與故障診斷提供了新的思路。實驗結果表明，該模型在分類準確率、魯棒性和泛化能力方面具有顯著優勢。多模態融合算法憑借其創新點和實驗驗證的有效性，在滾動軸承故障、變壓器油氣故障等領域展現出廣闊的應用前景，有望推動相關領域故障診斷技術的進一步發展。

關鍵詞：多模態融合；故障診斷；馬爾可夫場；卷積神經網絡；門控循環單元
適用平臺：Matlab2023版本及以上。實驗硬件設備配置如下：選用高性能計算機，搭載i7處理器，以確保數據處理和模型訓練的高效性；配備16GB的內存，滿足大規模數據加載和模型運算過程中的內存需求；使用高性能顯卡，提供強大的并行計算能力，加速深度學習模型的訓練過程。實驗參數的選擇依據多方面因素確定。對于卷積神經網絡（CNN）部分，卷積核的大小和數量經過多次預實驗調優，旨在提取有效特征的同時控制模型復雜度。池化層的步長和池化方式根據數據特點選擇，以保留關鍵信息并降低特征維度。門控循環單元（GRU）的隱藏單元數量通過實驗對比不同取值對模型性能的影響來確定，以平衡模型的表達能力和訓練難度。學習率的選擇參考相關文獻經驗并結合實際情況微調，以確保模型在訓練過程中能夠快速收斂且避免陷入局部最優。

程序設計

• 完整代碼私信博主回復基于MTF的1D-2D-CNN-GRU-Attention時序圖像多模態融合的故障識別，適合研究學習（Matlab完整源碼和數據），附模型研究報告

%% 構建序列輸入結構
% 故障數值序列
DecMat = SequenceSamples(:,1);
SequenceTrain = arrayDatastore(DecMat,"ReadSize",1,"OutputType","same");%打標簽并數據存儲
labTrain = TrainLabel';                                                              
LabelTrain = arrayDatastore(labTrain,"ReadSize",1,"OutputType","cell");%% 合并數值存儲塊
TrainDataStore = combine(imageTtain, SequenceTrain, LabelTrain);%% 測試集處理%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 時序信號處理
TestFeature =  TestData(FeCol, :);                         %% 特征
TestLabel   =  categorical(TestData(LaCol, :));            %% 標簽
N = size(TestLabel,2);%% 平鋪特征數據
Featuredata  = reshape(TestFeature,1,NumFea,1,N);   %% 時序數據格式轉換
NumSample  = N;                                            %% 樣本個數
for i=1:NumSampleSequenceSamples{i,1} = Featuredata(:,:,1,i);
end%% 構建圖像輸入結構
% 指定圖像文件夾路徑
imageDataFolder = '測試集';% 獲取文件夾中的圖像文件列表
imageFiles = dir(fullfile(imageDataFolder, '*.png'));      %% 假設圖像文件類型為png% 提取文件名（不包含文件擴展名）
fileNames = {imageFiles.name};
[~, fileNamesWithoutExt] = cellfun(@fileparts, fileNames, 'UniformOutput', false);% 將文件名轉換為數值
fileIndices = cellfun(@str2double, fileNamesWithoutExt);%% 構建序列輸入結構
% 故障數值序列
DecMat = SequenceSamples(:,1);
SequenceTest = arrayDatastore(DecMat,"ReadSize",1,"OutputType","same");%打標簽并數據存儲
labTest = TestLabel';                                                              
LabelTest = arrayDatastore(labTest,"ReadSize",1,"OutputType","cell");%% 構建 CNN并聯GRU 網絡
lgraph = layerGraph();% 添加層分支, 將網絡分支添加到層次圖中。每個分支均為一個線性層組。
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)

參考資料

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