MATLAB: FFT of a damping signal

dampedfft

Hello, I have got a damping time dependent signal(attached data) and I wish to get the FFT of the signal. Unfortunately right now I am not able to get the peak frequency. Can someone help me in finding the FFT please.

Best Answer

Try this:

D = load('FFT.txt');
t = D(:,1);
s = D(:,2);
Ts = mean(diff(t));                                                 % Sampling Time
Fs = 1/Ts;                                                          % Sampling Frequency
Fn = Fs/2;                                                          % Nyquist Frequency
L = length(t);
FTs = fft(s)/L;
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                                 % Frequency Vector
Iv = 1:length(Fv);                                                  % Index Vector
[pks,locs] = findpeaks(abs(FTs(Iv))*2, 'MinPeakHeight',1E-3);
figure(1)
loglog(Fv, abs(FTs(Iv))*2)
hold on
plot(Fv(locs), pks, '^r')
grid
text(Fv(locs)*1.1, pks, sprintf('\\leftarrow Peak = %.5f at %.3E Hz', pks, Fv(locs)), 'HorizontalAlignment','left')

Related Solutions

MATLAB: FFT \ Peak points

Please change the 'amplitude peaks' section of your code to the following:

% amplitude peaks
[pks,locs] = findpeaks(mx(1:nfft/2),'SortStr','descend');
n = min(n,length(pks));
subplot(2,1,2);
plot(f_fft,mx)
hold on
plot(f_fft(locs(1:n)),pks(1:n),'rv','MarkerFaceColor','r')
xlabel('Frequency ')
ylabel(' Amplitutde')

MATLAB: How can fit the data by multiple damping cosine functions

This is as close as I can get to a decent fit:

D = load('data_fft_peak6.mat');
t = D.data_fft_peak(:,1);
s = D.data_fft_peak(:,2);
figure
plot(t, s)
grid
L = numel(t);
Ts = mean(diff(t));
Fs = 1/Ts;
Fn = Fs/2;
N = 2^(nextpow2(L)+2);
FTs = fft(s,N)/L;
Fv = linspace(0, 1, fix(numel(FTs)/2)+1)*Fn;
Iv = 1:numel(Fv);
% [pks,locs] = findpeaks(abs(FTs(Iv))*2, 'MinPeakProminence',0.001);
[pks,locs] = findpeaks(abs(FTs(Iv))*2, 'MinPeakHeight',0.02);
figure
plot(Fv, abs(FTs(Iv))*2)
hold on
plot(Fv(locs), abs(FTs(locs))*2, 'r^')
hold off
grid
pklbls = compose('\\leftarrow Freq = %8.5f Hz\n     Ampl = %8.5f', [Fv(locs).', pks]);
text(Fv(locs), pks, pklbls, 'HorizontalAlignment','left', 'VerticalAlignment','top')
% objfcn = @(b,t) b(1).*cos(b(2)*t) + b(3).*cos(b(4).*t) + b(5).*cos(b(6).*t) .* b(7).*t.*exp(b(8).*t);
% B0 = [pks(1) Fv(locs(1))*2*pi pks(2) Fv(locs(2))*2*pi pks(3) Fv(locs(3))*2*pi 0.5 -0.01];
objfcn = @(b,t) b(1).*cos(b(2)*t) + b(3).*cos(b(4).*t) .* b(5).*t.*exp(b(6).*t);
B0 = [pks(1) Fv(locs(1))*2*pi pks(2) Fv(locs(2))*2*pi 0.5 -0.01].';
B = lsqcurvefit(objfcn, B0, t, s) 
fitfcn = objfcn(B, t);
figure
plot(t, s)
hold on
plot(t, fitfcn, '-r')
hold off
grid

Note — I included an extended version of ‘objfcn’ for you to experiment with.

The estimated parameters are:

B = 
   0.022930977208190
   0.106707811047044
  -0.000717587879530
   0.112740321550530
   1.119470956816299
  -0.002453356895356

EDIT — (29 Dec 2020 at 17:29)

Also experiment with:

[pks,locs] = findpeaks(abs(FTs(Iv))*2, 'MinPeakProminence',0.008);

and:

objfcn = @(b,t) b(1).*cos(b(2)*t) + b(3).*cos(b(4).*t) .* b(7).*t.*exp(b(8).*t) + b(5).*cos(b(6).*t);
B0 = [pks(1) Fv(locs(1))*2*pi pks(2) Fv(locs(2))*2*pi pks(3) Fv(locs(3))*2*pi 0.5 -0.01].';

producing:

B = 
   0.007599194766653
   0.010943887298747
  -0.000688462858777
   0.112676604541936
   0.035329767711197
   0.118358798142968
   1.119527877005960
  -0.002418300550008

Best Answer

Related Solutions

MATLAB: FFT \ Peak points

MATLAB: How can fit the data by multiple damping cosine functions

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