How to process the data that have same row name

Hello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:
% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = ‘file directory’;
% Get list of files in the directory
files = dir(fullfile(path, ‘*’));
% Loop through the files in the directory
for j = 1:length(files)
filename = files(j).name;
if files(j).isdir % Skip directories
continue;
end
file_path = fullfile(path, filename); % Full path of the file
dataset = readtable(file_path, ‘Delimiter’, ‘t’, ‘FileType’, ‘text’); % Read the dataset
% Loop through each bearing number
for j = 1:length(bearing_numbers)
bearing_no = bearing_numbers(j);
% Extract the bearing data
bearing_data = dataset{:, bearing_no};
% Calculate features
feature_matrix = [
max(bearing_data), % Max
min(bearing_data), % Min
mean(bearing_data), % Mean
std(bearing_data, 1), % Std
sqrt(mean(bearing_data.^2)), % RMS
compute_skewness(bearing_data), % Skewness
compute_kurtosis(bearing_data), % Kurtosis
max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
];
df = array2table(feature_matrix.’); % Transpose for correct orientation
df.Properties.VariableNames = {‘Max’, ‘Min’, ‘Mean’, ‘Std’, ‘RMS’, ‘Skewness’, ‘Kurtosis’, ‘CrestFactor’, ‘FormFactor’};
df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
df.Properties.RowNames = {rowName};
% Append the table to the corresponding bearing’s feature matrix
switch bearing_no
case 1
if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
Time_feature_matrix1 = [Time_feature_matrix1; df];
end
case 2
if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
Time_feature_matrix2 = [Time_feature_matrix2; df];
end
case 3
if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
Time_feature_matrix3 = [Time_feature_matrix3; df];
end
case 4
if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
Time_feature_matrix4 = [Time_feature_matrix4; df];
end
% case 1
% Time_feature_matrix1 = [Time_feature_matrix1; df];
% case 2
% Time_feature_matrix2 = [Time_feature_matrix2; df];
% case 3
% Time_feature_matrix3 = [Time_feature_matrix3; df];
% case 4
% Time_feature_matrix4 = [Time_feature_matrix4; df];
end
end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x – mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x – mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 – 3;
endHello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

Here the Code:
% Initialize empty tables for each bearing
Time_feature_matrix1 = table();
Time_feature_matrix2 = table();
Time_feature_matrix3 = table();
Time_feature_matrix4 = table();
% Specify the test set and bearing numbers
test_set = 3;
bearing_numbers = [1, 2, 3, 4];
% Set the path to the directory containing the data files
path = ‘file directory’;
% Get list of files in the directory
files = dir(fullfile(path, ‘*’));
% Loop through the files in the directory
for j = 1:length(files)
filename = files(j).name;
if files(j).isdir % Skip directories
continue;
end
file_path = fullfile(path, filename); % Full path of the file
dataset = readtable(file_path, ‘Delimiter’, ‘t’, ‘FileType’, ‘text’); % Read the dataset
% Loop through each bearing number
for j = 1:length(bearing_numbers)
bearing_no = bearing_numbers(j);
% Extract the bearing data
bearing_data = dataset{:, bearing_no};
% Calculate features
feature_matrix = [
max(bearing_data), % Max
min(bearing_data), % Min
mean(bearing_data), % Mean
std(bearing_data, 1), % Std
sqrt(mean(bearing_data.^2)), % RMS
compute_skewness(bearing_data), % Skewness
compute_kurtosis(bearing_data), % Kurtosis
max(bearing_data) / sqrt(mean(bearing_data.^2)), % CrestFactor
sqrt(mean(bearing_data.^2)) / mean(bearing_data) % FormFactor
];
df = array2table(feature_matrix.’); % Transpose for correct orientation
df.Properties.VariableNames = {‘Max’, ‘Min’, ‘Mean’, ‘Std’, ‘RMS’, ‘Skewness’, ‘Kurtosis’, ‘CrestFactor’, ‘FormFactor’};
df.Properties.RowNames = {[filename(1:end-4)]}; % Append bearing number
df.Properties.RowNames = {rowName};
% Append the table to the corresponding bearing’s feature matrix
switch bearing_no
case 1
if ~ismember(rowName, Time_feature_matrix1.Properties.RowNames)
Time_feature_matrix1 = [Time_feature_matrix1; df];
end
case 2
if ~ismember(rowName, Time_feature_matrix2.Properties.RowNames)
Time_feature_matrix2 = [Time_feature_matrix2; df];
end
case 3
if ~ismember(rowName, Time_feature_matrix3.Properties.RowNames)
Time_feature_matrix3 = [Time_feature_matrix3; df];
end
case 4
if ~ismember(rowName, Time_feature_matrix4.Properties.RowNames)
Time_feature_matrix4 = [Time_feature_matrix4; df];
end
% case 1
% Time_feature_matrix1 = [Time_feature_matrix1; df];
% case 2
% Time_feature_matrix2 = [Time_feature_matrix2; df];
% case 3
% Time_feature_matrix3 = [Time_feature_matrix3; df];
% case 4
% Time_feature_matrix4 = [Time_feature_matrix4; df];
end
end
end
% Define function to compute skewness
function skewness = compute_skewness(x)
n = length(x);
third_moment = sum((x – mean(x)).^3) / n;
s_3 = std(x, 1)^3;
skewness = third_moment / s_3;
end
% Define function to compute kurtosis
function kurtosis = compute_kurtosis(x)
n = length(x);
fourth_moment = sum((x – mean(x)).^4) / n;
s_4 = std(x, 1)^4;
kurtosis = fourth_moment / s_4 – 3;
end Hello. Currently I am doing a testing codes. But then, I found a loop problem which is too challenging for me since I am already facing it more than ten times (I am still a newbie). Below is my coding. My goals is to process the raw data and extract the features as stated in the code. Basically the problem that I am facing is that:
Error using tabular/vertcat Duplicate table row name: ‘2004.03.15.12.0’.
Error in Third_Test (line 55) Time_feature_matrix1 = [Time_feature_matrix1; df];

The file is too big so with that I share my link to GDrive: https://drive.google.com/drive/folders/1MltnJyAUh1BJpoPdNpqOUiHfhdbGF4wV?usp=drive_link

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