Error executing bearing failure detection code using deep learning transfer learning in MATLAB: How can I solve it?

Question

Alejandro 2023-8-27

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2013707-error-executing-bearing-failure-detection-code-using-deep-learning-transfer-learning-in-matlab-how

移动：DGM 2024-3-30

Hello everyone.

I'm working on a project where I'm trying to detect faults in bearings using transfer learning with deep learning in MATLAB (R2022b version). However, I've come across an issue a couple of times that has me a bit stuck, and I was wondering if you could provide some guidance.

The error message I'm receiving is literally:

Error in predmaint.internal.pmdata.PMDatastore/read (line192) thow(E)
Error in BearingFaultDiagnosisCWRU>convertSignalToScalogram data = read(ensemble)

I've gone through my code multiple times and verified that my data is in the correct format, the necessary libraries are imported, and the parameters are set up properly. Despite my efforts, the error persists, and I'm certain there must be something I'm overlooking. I also have seen in the community that someone have had a similar issue an maybe it can be related with directories thart Matlab is using but I'm not sure. I'd like to ask if they could provide me guidance to troubleshooting this specific error.

I appreciate any suggestions, advice, or hints you can provide in advance, and I attached below these three scripts:

BearingFaultDiagnosisCWRU (the main script where I'm working)
readMFPTBearing (auxiliary program to read specific data from a bearing file)
PMDatastore (only a part of the code until the error)

This is "BearingFaultDiagnosisCWRU":

% SCALOGRAM OF BEARING DATA %
% Import data with inner race fault
data_inner = load('C:\Program Files\MATLAB\R2022b\toolbox\predmaint\predmaintdemos\bearingFaultDiagnosis\RollingElementBearingFaultDiagnosis-Data-master\train_data\InnerRrace_0.mat');
% Plot bearing signal and scalogram
plotBearingSignalAndScalogram(data_inner)
% Import data with outer race fault
data_outer = load('C:\Program Files\MATLAB\R2022b\toolbox\predmaint\predmaintdemos\bearingFaultDiagnosis\RollingElementBearingFaultDiagnosis-Data-master\test_data\OuterRace_8.mat');
% Plot original signal and its scalogram
plotBearingSignalAndScalogram(data_outer)
% Import normal bearing data
data_normal = load('C:\Program Files\MATLAB\R2022b\toolbox\predmaint\predmaintdemos\bearingFaultDiagnosis\RollingElementBearingFaultDiagnosis-Data-master\train_data\baseline_0.mat');
% Plot original signal and its scalogram
plotBearingSignalAndScalogram(data_normal)
% PREPARE TRAINING DATA %
fileLocation = 'C:\Program Files\MATLAB\R2022b\toolbox\predmaint\predmaintdemos\bearingFaultDiagnosis\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');
% TRAIN NETWORK WITH TRANFER LEARNING %
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);
% VALIDATE USING TEST DATA SETS %
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)
% ADITIONAL 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); %<<<----------------------------------------------[HERE IS THE ERROR 2]
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

This is "readMFPTBearing":

function data = readMFPTBearing(filename, variables)
% Read variables from a file, used by the ReadFcn of a
% fileEnsembleDatastore object.
%
% Inputs:
%    filename  - a string of the file name to read from
%    variables - a string array containing variable names to read
%                
% Output:
%    data - return a table with a single row
%
% Note: This function uses dynamic field references to read data from
% files, see
% https://www.mathworks.com/help/matlab/matlab_prog/generate-field-names-from-variables.html
% for more information
data = table();
mfile = matfile(filename); % Allows partial loading
[~, fname] = fileparts(filename);
bearing = mfile.bearing;
for ct = 1:numel(variables)
    if strcmp(variables{ct},'FileName')
        %Return filename
        val = string(fname);
    elseif strcmp(variables{ct},'Label')
        %Set label based on filename
        cfname = char(fname);
        switch cfname(1:3)
            case 'Nor'
                val = "Normal";
            case 'Inn'
                val = "Inner Race Fault";
            case 'Out'
                val = "Outer Race Fault";
            otherwise
                val = "Unknown";
        end
    else
        
        % The data could be either stored at top level or within bearing structure
        if isfield(bearing, variables{ct})
            val = bearing.(variables{ct});
        else
            val = mfile.(variables{ct});
        end
        
        % Handle unstructured data
        % e.g. numeric data in string type or missing data
        if ischar(val)
            val = str2double(val);
        elseif isempty(val)
            val = nan;
        end
        
        if numel(val) > 1
            val = {val};
        end
    end
    data.(variables{ct}) = val;
end
end

This is a part of "PMDatastore" (Error in line 191 is indicated in comments at the end):

classdef PMDatastore < ...
        matlab.io.Datastore & ...
        matlab.io.datastore.Partitionable
    %PMDATASTORE
    %
    %    Datastore subclass used for predictive maintenance. Key extensions
    %    are to add notions of
    %        - data variables
    %        - independent variables
    %        - condition variables
    %
    
    % Copyright 2017-2021 The MathWorks, Inc.    
    
    properties (Access = protected)
        InternalDataset
        Index uint32
    end
    
    properties(Access = public, Dependent = true)
        %DATAVARIABLES Names of data variables in the ensemble
        %
        %    A string array of the names of data variables in the
        %    ensemble. The default value is an empty string array.
        %
        %    Data variables are the main content of the members in an
        %    ensemble and include measured data as well as derived data
        %    that is used for analysis and development of predictive
        %    maintenance algorithms. Examples are vibration signals, and
        %    derived values such as mean vibration value, or peak vibration
        %    frequency.
        %
        %    The data variable names are used to identify data in the ensemble
        %    and are also the variable names (column names) of the table
        %    returned by the ensemble read() method.
        %
        DataVariables string
        
        %INDEPENDENTVARIABLES Names of independent variables in the ensemble
        %
        %    A string array of the names of independent variables in the
        %    ensemble. The default value is an empty string array.
        %
        %    Independent variables are typically used to order the members
        %    in an ensemble. Examples are time stamps, number of operating
        %    hours, or miles driven.
        %
        %    The independent data variable names are used to identify data
        %    in the ensemble and are also the variable names (column names)
        %    of the table returned by the ensemble read() method.
        %
        IndependentVariables string
        
        %CONDITIONVARIABLES Names condition variables in the ensemble
        %
        %    A string array of the names of the condition variables in the
        %    ensemble. The default value is an empty string array.
        %
        %    Condition variables are used to label the members in a
        %    ensemble and are typically labels describing the fault
        %    condition of the ensemble member.
        %
        %    The condition variable names are used to identify data in the
        %    ensemble and are also the variable names (column names) of the
        %    table returned by the ensemble read() method.
        %
        ConditionVariables string
        
        %SelectedVariables Names of data variables to read from the ensemble
        %
        %    A string array of the names of data variables to read from the
        %    ensemble. The default value is an empty string array.
        %
        %    The SelectedVariables are used by the ensemble read() method
        %    to specify the data variables that the read() method returns.
        %    The read() method returns a table with table variables (table
        %    columns) for each name defined in SelectedVariables.
        %
        SelectedVariables string
        
        %ReadSize Number of members to read from the ensemble
        %
        %    A positive finite integer value specifying the number of
        %    members to read from the ensemble. The default value is 1.
        %
        %    The ReadSize is used by the ensemble read() method to
        %    determine the number of ensemble members that the read()
        %    method returns. The read() method returns a table with table
        %    ReadSize number of rows where each row is an ensemble member.
        %
       ReadSize(1,1) {mustBeNumeric, mustBePositive, mustBeFinite}
    end
    properties(GetAccess = public, SetAccess = protected, Dependent = true)
        %NumMembers Number of members in the ensemble
        %
        %    A read-only integer value giving the number of members in the
        %    ensemble.
        %
        NumMembers uint32
        
        %LastMemberRead 
        %
        %    A read-only value specifying the member that the ensemble last
        %    read. The value is [] when the ensemble is reset and updates
        %    when the ensemble read() command is used.
        %
        LastMemberRead
    end
    
    properties(Hidden, Dependent) % App use
       IVUnit(1,:) cell
       DVUnit(1,:) cell
       CVUnit(1,:) cell
    end
    
    properties(Access = {?predmaintguis.internal.data.DataInfo,?dfeapp.internal.data.DataInfo, ?predmaint.internal.pmdata.var.DatasetManager}) % App use
       VarInfoArray
    end
    
    properties(Access = protected)
        FcnData               function_handle %Function to retrieve data for specified member
        FcnIV                 function_handle %Function to retrieve IV value for specified member
        FcnCondition          function_handle %Function to retrieve condition data for specified member
        FcnCommon             function_handle %Function to retrieve all values for a specified member
        FcnWriteToMember      function_handle %Function to write data to a member
        DataVariables_        string
        IndependentVariables_ string
        ConditionVariables_   string
        SelectedVariables_    string
        ReadSize_             double = 1;
        VariableTypes
        
        % app use
        IVUnit_
        DVUnit_
        CVUnit_
    end
    
    methods (Abstract, Access = protected)
        tf = hasdata_(obj);
        n = getNumMembers_(obj);
        reset_(obj)
        Member = getNextMember(obj);
        Member = getLastMemberRead(obj);
        resetLastMemberRead(obj)
        [tf,msg] = supportsWrite(obj);
        sobj = subset_(obj,idx);
    end
    
    methods (Access = protected, Abstract, Static)
        info = getMemberInfo(data, Member);
    end
    
    %Datastore API methods
    methods
        function [data, info] = read(obj)
            %READ Read member data from the ensemble
            %
            %   Increment the ensemble current member and return data from
            %   the current member. 
            %
            %   The data read is specified by the SelectedVariables
            %   property. Only one independent variable can be specified by
            %   the SelectedVariables property.
            %
            %   [data,info] = read(obj)
            %
            %   Outputs:
            %      data - table row containing data read from the current
            %             entry in the data store
            %      info - a structure with the file name where the data was
            %             read from and size of the data
            %
            
            if ~hasdata(obj)
                error(message('predmaint:general:errPMDatastore_Empty'));
            end
            
            numRead = 0;
            resetLastMemberRead(obj)
            while numRead < obj.ReadSize
            
                if numRead > 0 && ~hasdata(obj)
                   %Got to end of datastore
                   break
                end
                Member = getNextMember(obj); % increments current member counter
                try
                    rdata = reader(obj,Member);
                    numRead = numRead+1;
                catch E
                    throw(E)  %<<<----------------------------------------[HERE IS ERROR 1]
                end
                rinfo = obj.getMemberInfo(rdata, Member, obj.Index);
                
                if numRead > 1
                    data = [data;rdata]; %#ok<AGROW>
                    info = [info;rinfo]; %#ok<AGROW>
                else
                    info = rinfo;
                    data = rdata;
                end
                
                if isempty(obj.Index)
                    obj.Index = 1;
                else
                    obj.Index = obj.Index+1;
                end
            end
        end
        

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Islem Az 2024-3-30

移动：DGM 2024-3-30

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

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Answer 1

Varun 2023-9-5

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2013707-error-executing-bearing-failure-detection-code-using-deep-learning-transfer-learning-in-matlab-how#answer_1301991

在 MATLAB Online 中打开

Hi,

I see that you're encountering an error in the "PMDatastore.m" file, specifically in the given section:

try 
    rdata = reader(obj,Member); 
    numRead = numRead+1; 
catch E 
    throw(E) %<<<--------------- [HERE IS ERROR 1] 
end 

As, you are calling “reader(obj,Member)” but it is neither defined in “PMDatastore” class methods, it is parent classes “matlab.io.Datastore” and “matlab.io.datastore.Partitionable” methods nor anywhere in the list of abstract methods that you have declared in “PMDatastore” class.

Hence, while executing in try block it is unable to find “reader” definition and goes to catch block where “throw(E)” is executed which throws an error and the program is terminated.

So, you should also define “reader” method in “PMDatastore” class or declare it as abstract so that any child class implementing “PMDatastore” class define “reader” method in it.

Hope this helps.

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Error executing bearing failure detection code using deep learning transfer learning in MATLAB: How can I solve it?

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Error executing bearing failure detection code using deep learning transfer learning in MATLAB: How can I solve it?

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