How can I fix this error: Error in predmaint.​internal.p​mdata.PMDa​tastore/re​ad (line 192)

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

I don't know why this error appear, and I couldn't find anything about it. Please help!

[Walter Roberson]

it cannot find the file perhaps?

[Alejandro]

The problem was like you said, and finally it worked. I really appreciate your help!!

[Xinyi]

Hello there. I am having the same problem here. Do you mind telling me how you modified the code to make it work? Thank you in advance>

[Islem Az]

Hello there. I am having the same problem here.can you help me?

Answer 1

[Juan Luis Pérez-Ruiz]

Maybe you are not correctly reading the dataset, thus the plots are not shown. In which line of the original code the error appears?