Why do I get zero frequencies in a fft when the constant offset is already removed?

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The goal is to find the frequencies (if any at all) in the data. When performing the fft, no significant peaks can be distinguished and repetitive "humps" are seen in the fft. Additionaly, there seem to be a lot of peaks near 0 Hz, which could be caused by a constant offset. However this is also removed with S= fft( data -mean(data)). I am looking for frequencies betweeh 0 and 500 Hz, as I sample with a frequency of around 1300 Hz (the total sample time is 300 seconds, and around 390000 samples are made.
What can I do to find valuable peaks in the fft?
Thanks in advance

Accepted Answer

Mathieu NOE
Mathieu NOE on 15 Jun 2022
hello
the fft seems to me inappropriate in this case. If the goal was to find the time difference between the succesive pulses , see the %%% NEW CODE %% I added at the bottom of your original code
this new portion of code will define the time stamps for positive and negative slope (aka rising / falling) trigger.
results are given by :
mean_period_pos = mean(diff(t0_pos)) % (seconds) / mean period computed on positive slope crossings
mean_period_neg = mean(diff(t0_neg)) % (seconds) / mean period computed on negative slope crossings
results
mean_period_pos = 21.5847 (seconds)
mean_period_neg = 21.6408 (seconds)
As we can see the period is more or less the same if we use the rising or falling slopes of the signal
full code
FYI I changed for another smoothing function as movmean is not in my package. You can of course go back to your own version
just make sure that the signal is clean were you want the trigger to operate (here at y = 0.5) . if your data has zigzags all over the place due to noise , the crossing function will generate uneeded extra trigger points
clear variables; close all; clc;
%% Uploading the data from the concentration measurements
data = readtable('Test20_Position3_2kg_90d_5mm_InducedVibration20s_09_06_2022');
t1 = table2array( data(:,2) )./1000000 - table2array(data(1,2))./1000000;
I1 = table2array( data(:,1));
%% linearising the data
I1_I0_1= (10.^((I1-37)/(458*0.5))-1)/100;
%% This section will remove the present noise, via a moving average
% Window = 21; %number of periods for the moving average
% Avrg_1 = movavg(I1_I0_1,'simple',Window);
Avrg_1 = smoothdata(I1_I0_1,'gaussian',100); % my code
%% Plotting all data: Laser signal I over time
figure()
hold on
plot(t1,Avrg_1)
legend('Position 3')
title('Laser signal I over time:Induced Pulsations each 20 seconds')
xlabel('Time [s]');
ylabel('I [-]')
grid on
%%
% Discrete Fourier Transform (DFT)
S = fft(Avrg_1);
% S = fft(detrend(Avrg_1));
Fs1 = length(Avrg_1)/300; % Sampling frequency
T = 1/Fs1; % Sampling period
L1 = length(Avrg_1); % Length of signal
f1 = Fs1*(0:(L1/2))/L1;
%% Converting magnitude to db and taking the left part of the mirrored Fourier transform
%Y = fft(X) computes the discrete Fourier transform (DFT) of X using a fast Fourier transform (FFT) algorithm.
%Left side of the spectrum
P2 = abs(S/L1);
P1 = P2(1:L1/2+1);
P1(2:end-1) = 2*P1(2:end-1);
%Removing lowest frequencies
for i = 1:length(P1)
if P1(i) < 10^(-10)
P1_angle(i) = 0;
end
end
%% FFT Plot: Magnitude (Db) versus frequency (Hz)
figure()
% hold on
semilogy(f1,P1) %Frequency plotted by Fourier Transform
title('Single-Sided Amplitude Spectrum: Position 5, 90 degrees')
xlabel('f (Hz)')
ylabel('|P1(f)|')
legend('Data1')
% xlim([0 600])
% ylim([0 0.0003])
%%% NEW CODE %%%
threshold = 0.5; % your value here
[t0_pos,s0_pos,t0_neg,s0_neg]= crossing_V7(Avrg_1,t1,threshold,'linear'); % positive (pos) and negative (neg) slope crossing points
% ind => time index (samples)
% t0 => corresponding time (x) values
% s0 => corresponding function (y) values , obviously they must be equal to "threshold"
figure()
plot(t1,Avrg_1,'b',t0_pos,s0_pos,'*r',t0_neg,s0_neg,'*g','linewidth',2,'markersize',12);grid on
legend('signal','signal positive slope crossing points','signal negative slope crossing points');
% subplot(2,1,2),plot(t_period,period,'linewidth',2,'markersize',12);grid on
mean_period_pos = mean(diff(t0_pos)) % (seconds) / mean period computed on positive slope crossings
mean_period_neg = mean(diff(t0_neg)) % (seconds) / mean period computed on negative slope crossings
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [t0_pos,s0_pos,t0_neg,s0_neg] = crossing_V7(S,t,level,imeth)
% [ind,t0,s0,t0close,s0close] = crossing_V6(S,t,level,imeth,slope_sign) % older format
% CROSSING find the crossings of a given level of a signal
% ind = CROSSING(S) returns an index vector ind, the signal
% S crosses zero at ind or at between ind and ind+1
% [ind,t0] = CROSSING(S,t) additionally returns a time
% vector t0 of the zero crossings of the signal S. The crossing
% times are linearly interpolated between the given times t
% [ind,t0] = CROSSING(S,t,level) returns the crossings of the
% given level instead of the zero crossings
% ind = CROSSING(S,[],level) as above but without time interpolation
% [ind,t0] = CROSSING(S,t,level,par) allows additional parameters
% par = {'none'|'linear'}.
% With interpolation turned off (par = 'none') this function always
% returns the value left of the zero (the data point thats nearest
% to the zero AND smaller than the zero crossing).
%
% check the number of input arguments
error(nargchk(1,4,nargin));
% check the time vector input for consistency
if nargin < 2 | isempty(t)
% if no time vector is given, use the index vector as time
t = 1:length(S);
elseif length(t) ~= length(S)
% if S and t are not of the same length, throw an error
error('t and S must be of identical length!');
end
% check the level input
if nargin < 3
% set standard value 0, if level is not given
level = 0;
end
% check interpolation method input
if nargin < 4
imeth = 'linear';
end
% make row vectors
t = t(:)';
S = S(:)';
% always search for zeros. So if we want the crossing of
% any other threshold value "level", we subtract it from
% the values and search for zeros.
S = S - level;
% first look for exact zeros
ind0 = find( S == 0 );
% then look for zero crossings between data points
S1 = S(1:end-1) .* S(2:end);
ind1 = find( S1 < 0 );
% bring exact zeros and "in-between" zeros together
ind = sort([ind0 ind1]);
% and pick the associated time values
t0 = t(ind);
s0 = S(ind);
if ~isempty(ind)
if strcmp(imeth,'linear')
% linear interpolation of crossing
for ii=1:length(t0)
%if abs(S(ind(ii))) >= eps(S(ind(ii))) % MATLAB V7 et +
if abs(S(ind(ii))) >= eps*abs(S(ind(ii))) % MATLAB V6 et - EPS * ABS(X)
% interpolate only when data point is not already zero
NUM = (t(ind(ii)+1) - t(ind(ii)));
DEN = (S(ind(ii)+1) - S(ind(ii)));
slope = NUM / DEN;
slope_sign(ii) = sign(slope);
t0(ii) = t0(ii) - S(ind(ii)) * slope;
s0(ii) = level;
end
end
end
% extract the positive slope crossing points
ind_pos = find(sign(slope_sign)>0);
t0_pos = t0(ind_pos);
s0_pos = s0(ind_pos);
% extract the negative slope crossing points
ind_neg = find(sign(slope_sign)<0);
t0_neg = t0(ind_neg);
s0_neg = s0(ind_neg);
else
% empty output
ind_pos = [];
t0_pos = [];
s0_pos = [];
% extract the negative slope crossing points
ind_neg = [];
t0_neg = [];
s0_neg = [];
end
end
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