How can I fix this error: Error in predmaint.internal.pmdata.PMDatastore/read (line 192)

Question

Alejandro 2023-8-13

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2008077-how-can-i-fix-this-error-error-in-predmaint-internal-pmdata-pmdatastore-read-line-192

回答： Juan Luis Pérez-Ruiz 2024-4-2

I am trying to replicate the example "Rolling Element Bearing Fault Diagnosis using Deep Learning" to learn and apply it in my own project. I am using R2022b Matlab version. However when I run exactly the script with the same data, these errors come up at Command Window:

Error in predmaint.internal.pmdata.PMDatastore/read (line 192)

throw(E)

Error in EjemploBearingFaultDL>convertSignalToScalogram (line 122)

data = read(ensamble);

Error in EjemploBearingFaultDL (line 43)

convertSignalToScalogram(ensembleTrain,folderName);

Below is the code of the example, but it can found here:

https://es.mathworks.com/help/predmaint/ug/rolling-element-bearing-fault-diagnosis-using-deep-learning.html#mw_rtc_RollingElementBearingFaultDiagnosisUsingDeepLearningExample_M_99EF7D93

% Import data with inner race fault
data_inner = load(fullfile(matlabroot, 'toolbox', 'predmaint', ...
    'predmaintdemos', 'bearingFaultDiagnosis', ...
    'train_data', 'InnerRaceFault_vload_1.mat'));
% Plot bearing signal and scalogram
plotBearingSignalAndScalogram(data_inner)
% Import data with outer race fault
data_outer = load(fullfile(matlabroot, 'toolbox', 'predmaint', ...
    'predmaintdemos', 'bearingFaultDiagnosis', ...
    'test_data', 'OuterRaceFault_3.mat'));
% Plot original signal and its scalogram
plotBearingSignalAndScalogram(data_outer)
% Import normal bearing data
data_normal = load(fullfile(matlabroot, 'toolbox', 'predmaint', ...
    'predmaintdemos', 'bearingFaultDiagnosis', ...
    'train_data', 'baseline_1.mat'));
% Plot original signal and its scalogram
plotBearingSignalAndScalogram(data_normal)
if exist('RollingElementBearingFaultDiagnosis-Data-master.zip', 'file')
    unzip('RollingElementBearingFaultDiagnosis-Data-master.zip')
end
fileLocation = fullfile('.', 'RollingElementBearingFaultDiagnosis-Data-master', 'train_data');
fileExtension = '.mat';
ensembleTrain = fileEnsembleDatastore(fileLocation, fileExtension);
ensembleTrain.ReadFcn = @readMFPTBearing;
ensembleTrain.DataVariables = ["gs", "sr", "rate", "load", "BPFO", "BPFI", "FTF", "BSF"];
ensembleTrain.ConditionVariables = ["Label", "FileName"];
ensembleTrain.SelectedVariables = ["gs", "sr", "rate", "load", "BPFO", "BPFI", "FTF", "BSF", "Label", "FileName"]
reset(ensembleTrain)
while hasdata(ensembleTrain)
  folderName = 'train_image';
  convertSignalToScalogram(ensembleTrain,folderName);
end
% Create image datastore to store all training images
path = fullfile('.', folderName);
imds = imageDatastore(path, ...
  'IncludeSubfolders',true,'LabelSource','foldernames');
% Use 20% training data as validation set
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.8,'randomize');
net = squeezenet
analyzeNetwork(net)
lgraph = layerGraph(net);
numClasses = numel(categories(imdsTrain.Labels));
newConvLayer = convolution2dLayer([1, 1],numClasses,'WeightLearnRateFactor',10,'BiasLearnRateFactor',10,"Name",'new_conv');
lgraph = replaceLayer(lgraph,'conv10',newConvLayer);
newClassificationLayer = classificationLayer('Name','new_classoutput');
lgraph = replaceLayer(lgraph,'ClassificationLayer_predictions',newClassificationLayer);
options = trainingOptions('sgdm', ...
  'InitialLearnRate',0.0001, ...
  'MaxEpochs',4, ...
  'Shuffle','every-epoch', ...
  'ValidationData',imdsValidation, ...
  'ValidationFrequency',30, ...
  'Verbose',false, ...
  'MiniBatchSize',20, ...
  'Plots','training-progress');
net = trainNetwork(imdsTrain,lgraph,options);
fileLocation = fullfile('.', 'RollingElementBearingFaultDiagnosis-Data-master', 'test_data');
fileExtension = '.mat';
ensembleTest = fileEnsembleDatastore(fileLocation, fileExtension);
ensembleTest.ReadFcn = @readMFPTBearing;
ensembleTest.DataVariables = ["gs", "sr", "rate", "load", "BPFO", "BPFI", "FTF", "BSF"];
ensembleTest.ConditionVariables = ["Label", "FileName"];
ensembleTest.SelectedVariables = ["gs", "sr", "rate", "load", "BPFO", "BPFI", "FTF", "BSF", "Label", "FileName"];
reset(ensembleTest)
while hasdata(ensembleTest)
  folderName = 'test_image';
  convertSignalToScalogram(ensembleTest,folderName);
end
path = fullfile('.','test_image');
imdsTest = imageDatastore(path, ...
  'IncludeSubfolders',true,'LabelSource','foldernames');
YPred = classify(net,imdsTest,'MiniBatchSize',20);
YTest = imdsTest.Labels;
accuracy = sum(YPred == YTest)/numel(YTest)
figure
confusionchart(YTest,YPred)
% AUXILIARY FUNCTIONS %
function plotBearingSignalAndScalogram(data)
% Convert 1-D bearing signals to scalograms through wavelet transform
fs = data.bearing.sr;
t_total = 0.1; % seconds
n = round(t_total*fs);
bearing = data.bearing.gs(1:n);
[cfs,frq] = cwt(bearing,'amor', fs);
% Plot the original signal and its scalogram
figure
subplot(2,1,1)
plot(0:1/fs:(n-1)/fs,bearing)
xlim([0,0.1])
title('Vibration Signal')
xlabel('Time (s)')
ylabel('Amplitude')
subplot(2,1,2)
surface(0:1/fs:(n-1)/fs,frq,abs(cfs))
shading flat
xlim([0,0.1])
ylim([0,max(frq)])
title('Scalogram')
xlabel('Time (s)')
ylabel('Frequency (Hz)')
end
function convertSignalToScalogram(ensemble,folderName)
% Convert 1-D signals to scalograms and save scalograms as images
data = read(ensemble);
fs = data.sr;
x = data.gs{:};
label = char(data.Label);
fname = char(data.FileName);
ratio = 5000/97656;
interval = ratio*fs;
N = floor(numel(x)/interval);
% Create folder to save images
path = fullfile('.',folderName,label);
if ~exist(path,'dir')
  mkdir(path);
end
for idx = 1:N
  sig = envelope(x(interval*(idx-1)+1:interval*idx));
  cfs = cwt(sig,'amor', seconds(1/fs));
  cfs = abs(cfs);
  img = ind2rgb(round(rescale(flip(cfs),0,255)),jet(320));
  outfname = fullfile('.',path,[fname '-' num2str(idx) '.jpg']);
  imwrite(imresize(img,[227,227]),outfname);
end
end