Problem when using FFT command?

Hi every one. I have a question about fast fourier transformer, when I calculate my time series data fast fourier transformer I get 3 Hz as maximum amplitute of frequencies although my sampling time is 1 second or 1 Hz, it is not acceptable have 3 Hz frequency when my sampling time is 1 Hz, please tell me what is my problem? Best Regards.

[Mathieu NOE]

hello

FYI

a generic code to do spectral (fft) analysis

you can skip delete the spectrogram if it's not useful

[mostafa]

Thank you so much Mathieu.

I have a m-file code that has writen with me, for simple input-output system like y=sin(2*t) it works good but for a big data series with 200/000 real datas it gave 3 Hz as system main frequency, but I know the sampling time is 1 Hz, how it possible to sample 3 Hz with 1 Hz sampling time? what is my mistake?

I attached my M-file.

Best Regards.

[Mathieu NOE]

hello

sorry I don't understand what your function is doing

could you share also the data (sampled at 1 Hz) ?

[mostafa]

Hello. Here you are also thanks alot for your attention. In_test file is my input Matrix and Out_test is my output Matrix. The sampling time is 1 second for each Matrix.

Best Regards.

[Mathieu NOE]

hello

according to my findings the output signal is mostly a dc component with some dynamic content up to 0.15 Hz

for the input signal is even lower

code below :

clc
clear all
close all

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% options 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% spectrogram dB scale
spectrogram_dB_scale = 80;  % dB range scale (means , the lowest displayed level is XX dB below the max level)


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

%% test data
% In_test file is my input Matrix and Out_test is my output Matrix. 
% The sampling time is 1 second for each Matrix.
Fs = 1;
% load In_test.mat
% signal = in3;

load Out_test.mat
signal = out3;

samples = length(signal);
time =(1:samples)/Fs;

figure(1),plot(time,signal,'b');grid

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 128;    
NOVERLAP = round(0.95*NFFT);
w = hanning(NFFT);     % Hanning window / Use the HANN function to get a Hanning window which has the first and last zero-weighted samples.



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : averaged FFT spectrum before filter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[sensor_spectrum, freq] = pwelch(signal,w,NOVERLAP,NFFT,Fs,'power');
sensor_spectrum_dB = 10*log10(sensor_spectrum);% convert to dB scale (ref = 1)

figure(2),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(' dB')

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 2 : time / frequency analysis : spectrogram demo
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[sg,fsg,tsg] = specgram(signal,NFFT,Fs,hanning(NFFT),NOVERLAP);  

% 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

% saturation of the dB range : 
min_disp_dB = round(max(max(sg_dBpeak))) - spectrogram_dB_scale;
sg_dBpeak(sg_dBpeak<min_disp_dB) = min_disp_dB;

% plots spectrogram
figure(3);
imagesc(tsg,fsg,sg_dBpeak);colormap('jet');
axis('xy');colorbar('vert');grid
title(['Spectrogram / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(fsg(2)-fsg(1)) ' Hz ']);
xlabel('Time (s)');ylabel('Frequency (Hz)');