一种基于新型1D-LBP的混合深度学习轴承故障检测方法matlab实现.zip

% clear all; % clc; % load ('ECGData.mat'); % hucreler={}; % for ii=1:size(tumdata,1) % dt=tumdata(ii,1:end-1); % [l1 l2 l3 l4 l5,l6,l7,l8,l9,l10] =klbp(dt,4); % X=[l6;l7;l8;l9;l10]; % hucreler{ii}=num2cell(X); % end % hucreler=hucreler'; % hucreler=[hucreler num2cell(tumdata(:,end))]; load ("huc.mat"); hucreler=huc; [m,n] = size(hucreler) ; P = 0.50 ; idx = randperm(m) ; XT = hucreler(idx(1:round(P*m)),:) ; XTs = hucreler(idx(round(P*m)+1:end),:) ; XTrain=XT(:,1); YTrain=categorical(cell2mat(XT(:,2))); XTest=XTs(:,1); YTest=categorical(cell2mat(XTs(:,2))); %1DCNN********************************************************* classes = categories(YTrain); numClasses = numel(classes) numFeatures = size(XTrain{1},1); filterSize = 3; numFilters = 32; layers = [ ... sequenceInputLayer(numFeatures) convolution1dLayer(filterSize,numFilters,Padding="causal") reluLayer layerNormalizationLayer convolution1dLayer(filterSize,2*numFilters,Padding="causal") reluLayer layerNormalizationLayer globalAveragePooling1dLayer fullyConnectedLayer(numClasses) softmaxLayer classificationLayer]; miniBatchSize = 127; options = trainingOptions("sgdm", ... MiniBatchSize=miniBatchSize, ... MaxEpochs=70, ... SequencePaddingDirection="left", ... ValidationData={XTest,YTest}, ... Plots="training-progress", ... Verbose=0); net = trainNetwork(XTrain,YTrain,layers,options); YPredTrain = classify(net,XTrain, ... MiniBatchSize=miniBatchSize, ... SequencePaddingDirection="left"); confusionchart(YTrain,YPredTrain,'ColumnSummary','column-normalized',... 'RowSummary','row-normalized','Title','Confusion Matrix for Train Set with 1D-CNN') figure YPredTest = classify(net,XTest, ... MiniBatchSize=miniBatchSize, ... SequencePaddingDirection="left"); confusionchart(YTest,YPredTest,'ColumnSummary','column-normalized',... 'RowSummary','row-normalized','Title','Confusion Matrix for Test Set with 1D-CNN') %1DCNN****************************************************** %GradCam numChannels = size(huc{1},2); numObservations = numel(huc(:,1)); figure tiledlayout(2,2) for i = 1:4 nexttile stackedplot(huc{i,1},DisplayLabels="Channel "+(1:numChannels)); title("Observation "+i) xlabel("Time Step") end numFailuresToShow = 2; isCorrect = YPredTest =="1" & YTest=="1" ; idxValidationFailure = find(isCorrect,numFailuresToShow); for i = 1:numFailuresToShow figure % t = tiledlayout(numChannels,1); idx = idxValidationFailure(i); modifiedSignal = XTest{idx}; modifiedSignal=modifiedSignal'; importance = gradCAM(net,modifiedSignal,"1"); for j = 1:numChannels nexttile plotWithColorGradient(modifiedSignal(j,:),importance'); ylabel("Channel "+j) xlabel("Time Steps") if ~isempty(failureLocationValidation{idx}) xline(failureLocationValidation{idx}(1),":") xline(failureLocationValidation{idx}(end),":") end end title(t,"Grad-CAM: Validation Observation "+idx) c = colorbar; c.Layout.Tile = "east"; c.Label.String = "Grad-CAM Importance"; end