MATLAB: How to use excel data in matlab for fast fourier transform

fftvibration

Hi everybody

i have attached a excel file. In this file, the first column is my magnitute in micron. I have got 71 magnitude values and all recorded in 0.5 seconds.I would like to use FFT, to change from time domain into frequency domain.

Sampling frequency is 142.

Can anyone help me out how to do that?

Kind regards.

Best Answer

[V,H]=xlsread('Vibration_analysis_FFT.xlsx');       % read data
H                                                   % headers -- ok, says lateral, axial in first two column
T=mean(diff(V(:,3)))                                % sample rate.. 0.005 --> 5 ms
Fs=1/T;                                             % sampling frequency, 200 Hz
L=length(V);                                        % length of sample
t=(0:L-1)*T;                                        % time vector -- not really needed
Y=fft(V(:,1:2));                                    % FFt the two accelerations
P2=abs(Y/L);                                        % 2-sided PSD estimator
P1=P2(1:fix(L/2)+1,:);                              % 1-sided
P1(2:end-1)=2*P1(2:end-1);                          % (-)freq half the energy but only only one DC;Fmax bin
f=Fs*(0:fix(L/2))/L;                                % the frequency at 200 Hz for L points
figure
plot(f,P1)

Shows have mostly just a DC component (non-zero mean) whch, parenthetically, means the sensor must be moving somewhere...let's compensate for the mean and see if helps...don't think will a lot because there's still mostly just a 1/f rolloff, no real spectral content in the signal...

Y=fft(V(:,1:2)-mean(V(:,1:2)));                    % remove mean before FFT
P2=abs(Y/L);
P1=P2(1:fix(L/2)+1,:);
figure
plot(f,P1)

As thought, just wipes the one commponent out, but leaves most other low frequency stuff. Try just a little more resolution by interpolationg since is very short signal...

N=128;
Y=fft(V(:,1:2)-mean(V(:,1:2)),N);                      % use 128-pt FFT
P2=abs(Y/L);                                  % all the energy still in L  time samples
P1=P2(1:fix(N/2)+1,:);
P1(2:end-1)=2*P1(2:end-1);
f=Fs*(0:fix(N/2))/N;
figure
plot(f,P1)

Shows a little more structure, maybe -- whether any of it is real is probably questionable I'd guess...what is the measurement of?

Related Solutions

MATLAB: FFT – am i doing anything wrong

Hi Adam, the main problem is that your data does not have zero mean.

This means that the zero frequency component (which isn't very interesting) is going to obscur what is probably more interesting.

first do this:

y = detrend(y,0);

Then take the Fourier transform of that zero-mean y.

To answer your other questions:

When you are dealing with real-valued data, the magnitudes of the Fourier transform are an even function of frequency so you only have to plot 1/2 the frequencies.

plot(f,2*abs(Y(1:NFFT/2+1)))

And the following two lines are just setting the number of points for the fft() and creating a meaningful frequency vector so you can plot the magnitudes as a function of frequency in Hz.

    NFFT = 2^nextpow2(L); % Next power of 2 from length of y
    f = Fs/2*linspace(0,1,NFFT/2+1);

MATLAB: I get the peaks at the wrong frequency and with the wrong amplitude.

hello Anna

look at my fft function at the end of my code

clc
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%





% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% data
data = importdata('SFC5S_nov25_ST_1_1.txt');
dt = 1e-6; % 1 micro seconds
signal = data(:,1);
samples = length(signal);
Fs = 1/dt; % sampling frequency (Hz)
%% decimate (if needed)
% NB : decim = 1 will do nothing (output = input)
decim = 1;
if decim>1
    signal = decimate(signal,decim);
    Fs = Fs/decim;
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 512;    % 
OVERLAP = 0.95;
% spectrogram dB scale
spectrogram_dB_scale = 80;  % dB range scale (means , the lowest displayed level is XX dB below the max level)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% options 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% if you are dealing with acoustics, you may wish to have A weighted
% spectrums 
% option_w = 0 : linear spectrum (no weighting dB (L) )
% option_w = 1 : A weighted spectrum (dB (A) )
option_w = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 1 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[freq, sensor_spectrum] = myfft_peak(signal,Fs,NFFT,OVERLAP);
% convert to dB scale (ref = 1)
sensor_spectrum_dB = 20*log10(sensor_spectrum);
% apply A weigthing if needed

if option_w == 1
    pondA_dB = pondA_function(freq);
    sensor_spectrum_dB = sensor_spectrum_dB+pondA_dB;
    my_ylabel = ('Amplitude (dB (A))');
else
    my_ylabel = ('Amplitude (dB (L))');
end
figure(1),plot(freq,sensor_spectrum_dB,'b');grid
title(['Averaged FFT Spectrum  / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
xlabel('Frequency (Hz)');ylabel(my_ylabel);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% display 2 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[sg,fsg,tsg] = specgram(signal,NFFT,Fs,hanning(NFFT),floor(NFFT*OVERLAP));  
% FFT normalisation and conversion amplitude from linear to dB (peak)
sg_dBpeak = 20*log10(abs(sg))+20*log10(2/length(fsg));     % NB : X=fft(x.*hanning(N))*4/N; % hanning only
% apply A weigthing if needed
if option_w == 1
    pondA_dB = pondA_function(fsg);
    sg_dBpeak = sg_dBpeak+(pondA_dB*ones(1,size(sg_dBpeak,2)));
    my_title = ('Spectrogram (dB (A))');
else
    my_title = ('Spectrogram (dB (L))');
end
% saturation of the dB range : 
% saturation_dB = 60;  % dB range scale (means , the lowest displayed level is XX dB below the max level)
min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;
sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;
% plots spectrogram
figure(2);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid
title([my_title ' / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');
function pondA_dB = pondA_function(f)
	% dB (A) weighting curve
	n = ((12200^2*f.^4)./((f.^2+20.6^2).*(f.^2+12200^2).*sqrt(f.^2+107.7^2).*sqrt(f.^2+737.9^2)));
	r = ((12200^2*1000.^4)./((1000.^2+20.6^2).*(1000.^2+12200^2).*sqrt(1000.^2+107.7^2).*sqrt(1000.^2+737.9^2))) * ones(size(f));
	pondA = n./r;
	pondA_dB = 20*log10(pondA(:));
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 overlap % (between 0 and 0.95)
signal = signal(:);
samples = length(signal);
% fill signal with zeros if its length is lower than nfft
if samples<nfft
    s_tmp = zeros(nfft,1);
    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;
        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

Best Answer

Related Solutions

MATLAB: FFT – am i doing anything wrong

MATLAB: I get the peaks at the wrong frequency and with the wrong amplitude.

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