I have data in time domain, and wants to convert it in frequency domain using FFT analysis, can any one please help me out

Hi everyone, I have data in time domain and wants to convert it in frequency domain using FFT analysis. I tried several time to apply fft but failed. Can anyone please help me out in solving?

There are three excel sheets DSO0017.csv, DSO0018.csv, and DSO0019.csv. and the code for time domain is given below:

Time Domain:

clear all

clc

%WITH HOLE UP:

anum1= xlsread('DSO0017.csv','A4:A20480');

anum2=xlsread('DSO0017.csv','C4:C20480');

anum3=smoothdata(anum2);

bnum1= xlsread('DSO0018.csv','A4:A20480');

bnum2=xlsread('DSO0018.csv','C4:C20480');

bnum3=smoothdata(bnum2);

cnum1= xlsread('DSO0019.csv','A4:A20480');

cnum2=xlsread('DSO0019.csv','C4:C20480');

cnum3=smoothdata(cnum2);

subplot(3,1,1)

plot(anum1,anum3,'b')

subplot(3,1,2)

plot(bnum1,bnum3,'r')

subplot(3,1,3)

plot(bnum1,bnum3,'b')

Can anyone please help me out to convert this time domain data to frequency domain, We used column 1 and 3 in this data.

Thanks in advance.

Answer 1

[Mathieu NOE]

hello

try this / adapt to your needs

NB : the sampling rate was not defined in the post , so change it if needed

all the best

clc
clearvars

   
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 2048;    % 
OVERLAP = 0.75;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

filename = ('DSO0017.csv'); 
data = readmatrix(filename);
points = data(:,1);
signal = data(:,3);
Fs = 1000; % sampling rate 
dt = 1/Fs;

[samples,channels] = size(signal);

% time vector 
time = (0:samples-1)*dt;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 1 : time domain plot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1),
plot(time,signal);grid on
title(['Time plot  / Fs = ' num2str(Fs) ' Hz ']);
xlabel('Time (s)');ylabel('Amplitude');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 2 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[freq, spectrum_raw] = myfft_peak(signal,Fs,NFFT,OVERLAP);

figure(2),plot(freq,20*log10(spectrum_raw));grid on
df = freq(2)-freq(1); % frequency resolution 
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz ']);
xlabel('Frequency (Hz)');ylabel('Amplitude (dB (L))');

function  [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal  (example sinus amplitude 1   = 0 dB after fft).
% Linear averaging
%   signal - input signal, 
%   Fs - Sampling frequency (Hz).
%   nfft - FFT window size
%   Overlap - buffer percentage of overlap % (between 0 and 0.95)

[samples,channels] = size(signal);

% fill signal with zeros if its length is lower than nfft
if samples<nfft
    s_tmp = zeros(nfft,channels);
    s_tmp((1:samples),:) = signal;
    signal = s_tmp;
    samples = nfft;
end

% window : hanning
window = hanning(nfft);
window = window(:);

%    compute fft with overlap 
 offset = fix((1-Overlap)*nfft);
 spectnum = 1+ fix((samples-nfft)/offset); % Number of windows
%     % for info is equivalent to : 
%     noverlap = Overlap*nfft;
%     spectnum = fix((samples-noverlap)/(nfft-noverlap));	% Number of windows

    % main loop
    fft_spectrum = 0;
    for i=1:spectnum
        start = (i-1)*offset;
        sw = signal((1+start):(start+nfft),:).*(window*ones(1,channels));
        fft_spectrum = fft_spectrum + (abs(fft(sw))*4/nfft);     % X=fft(x.*hanning(N))*4/N; % hanning only 
    end
    fft_spectrum = fft_spectrum/spectnum; % to do linear averaging scaling

% one sidded fft spectrum  % Select first half 
    if rem(nfft,2)    % nfft odd
        select = (1:(nfft+1)/2)';
    else
        select = (1:nfft/2+1)';
    end
fft_spectrum = fft_spectrum(select,:);
freq_vector = (select - 1)*Fs/nfft;
end

Answer 2

[Umair Dildar]

Thanks again.

The matlab file gives an empty graph, am I missing something?

[Mathieu NOE]

hello again

funny , I tried my code with the 3 posted csv file without issue

did you check that "signal" is not empty after loading a new file ? we need three columns when loading the csv file or adapt the code if the number of columns are different (and which one you want to display)

[Umair Dildar]

I want to use subplot for all the three files, and plot them seperately

[Mathieu NOE]

hello

here your are my friend

remember to change Fs is it's not correct (not defined in your post)

clc
clearvars

   
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 2048;    % 
OVERLAP = 0.75;


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Fs = 1000; % sampling rate 
dt = 1/Fs;

filenames = {'DSO0017.csv';'DSO0018.csv';'DSO0019.csv'}; % list of data files to process
nn  = numel(filenames);

for ci = 1:nn
    filename = filenames{ci};
    data = readmatrix(filename);
    points = data(:,1);
    signal = data(:,3);


    [samples,channels] = size(signal);

    % time vector 
    time = (0:samples-1)*dt;


    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % display 1 : time domain plot
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    figure(1),
    subplot(nn,1,ci);plot(time,signal);grid on
    title(['Time plot  / Fs = ' num2str(Fs) ' Hz ']);
    xlabel('Time (s)');ylabel('Amplitude');

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % display 2 : averaged FFT spectrum
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    [freq, spectrum_raw] = myfft_peak(signal,Fs,NFFT,OVERLAP);

    figure(2),
    subplot(nn,1,ci);plot(freq,20*log10(spectrum_raw));grid on
    df = freq(2)-freq(1); % frequency resolution 
    title([filename ' : Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz ']);
    xlabel('Frequency (Hz)');ylabel('Amplitude (dB)');
    
end

function  [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal  (example sinus amplitude 1   = 0 dB after fft).
% Linear averaging
%   signal - input signal, 
%   Fs - Sampling frequency (Hz).
%   nfft - FFT window size
%   Overlap - buffer percentage of overlap % (between 0 and 0.95)

[samples,channels] = size(signal);

% fill signal with zeros if its length is lower than nfft
if samples<nfft
    s_tmp = zeros(nfft,channels);
    s_tmp((1:samples),:) = signal;
    signal = s_tmp;
    samples = nfft;
end

% window : hanning
window = hanning(nfft);
window = window(:);

%    compute fft with overlap 
 offset = fix((1-Overlap)*nfft);
 spectnum = 1+ fix((samples-nfft)/offset); % Number of windows
%     % for info is equivalent to : 
%     noverlap = Overlap*nfft;
%     spectnum = fix((samples-noverlap)/(nfft-noverlap));	% Number of windows

    % main loop
    fft_spectrum = 0;
    for i=1:spectnum
        start = (i-1)*offset;
        sw = signal((1+start):(start+nfft),:).*(window*ones(1,channels));
        fft_spectrum = fft_spectrum + (abs(fft(sw))*4/nfft);     % X=fft(x.*hanning(N))*4/N; % hanning only 
    end
    fft_spectrum = fft_spectrum/spectnum; % to do linear averaging scaling

% one sidded fft spectrum  % Select first half 
    if rem(nfft,2)    % nfft odd
        select = (1:(nfft+1)/2)';
    else
        select = (1:nfft/2+1)';
    end
fft_spectrum = fft_spectrum(select,:);
freq_vector = (select - 1)*Fs/nfft;
end