How to find the frequency and amplitude of an oscillating signal?
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Rohan Kokate
on 29 Nov 2023
Commented: Star Strider
on 1 Dec 2023
Hello,
I have a pressure signal which oscillates at a certain frequency and amplitude at steady-state. Is there a way to use the raw data (pressure and time) to find the frequency and amplitude of the oscillations. The raw data does have a little noise. I have attached an example image of the signal that I have along with an excel sheet which has the raw data. I believe FFT is the way to go but I have zero experience/background in using it.
Any help would be greatly appreciated.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/1555877/image.png)
Thank you in advance!
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Accepted Answer
Star Strider
on 29 Nov 2023
Edited: Star Strider
on 29 Nov 2023
One approach —
T1 = readtable('book1.xlsx', 'VariableNamingRule','preserve')
VN = T1.Properties.VariableNames;
t = T1{:,1};
s = T1{:,2};
figure
plot(t,s)
grid
checkTime = [mean(diff(t)) std(diff(t))] % Check Sampling Time Variation
Fs = 1/mean(diff(t)); % Mean Sampling Frequency
[sr,tr] = resample(s,t,Fs); % Resample To Constant Sampling Intervals (Required For Signal Ptocessing)
Fn = Fs/2; % Nyquist Frequency
L = size(T1,1); % Signal LEngth
NFFT = 2^nextpow2(L); % Use This Value To Make The 'fft' More Efficient
FTs = fft((s-mean(s)).*hann(L), NFFT)/L; % Discrete Windowed Fourier Transform
Fv = linspace(0, 1, NFFT/2+1)*Fn; % Frequency Vector
Iv = 1:numel(Fv); % Index Vector
[pks,locs,width] = findpeaks(abs(FTs(Iv))*2, 'MinPEakProminence',0.25, 'WidthReference','halfheight');
Peak_Frequency = Fv(locs)
Peak_Magnitude = pks
Peak_Width = width*Fv(2)
idx = find(diff(sign(abs(FTs(Iv))*2-(pks/2))));
for k = 1:numel(idx)
idxrng = max(1,idx(k)-1) : min(idx(k)+1,Iv(end));
hpf(k) = interp1(abs(FTs(idxrng))*2, Fv(idxrng), pks/2);
end
figure
plot(Fv, abs(FTs(Iv))*2)
hold on
plot(hpf, [1 1]*pks/2, '-k')
hold off
grid
xlabel('Frequency (Hz)')
ylabel('Magnitude')
title('Fourier Transform')
text(Peak_Frequency, Peak_Magnitude, sprintf('\\leftarrow Frequency = %.3f Hz\n Magnitude = %.3f',Peak_Frequency, Peak_Magnitude), 'Horiz','left')
text(hpf(2), pks/2, sprintf('\\leftarrow FWHM = %.3f Hz', Peak_Width))
EDIT — (29 Nov 2023 at 18:28)
Added text calls to Forueir Transform plot.
.
8 Comments
Star Strider
on 1 Dec 2023
As always, my pleasure!
Essentially, the more frequencies that exist in a signal, the more the total energy is distributed among them.
More Answers (1)
Paul
on 30 Nov 2023
Edited: Paul
on 30 Nov 2023
Hi Rohan,
For this signal, the output of fft without windowing gives a very good estimate of the amplitude and frequency of the signal.
Read and plot the data.
T = readtable('book1.xlsx', 'VariableNamingRule','preserve');
t = T{:,1};
p5 = T{:,2};
figure
plot(t,p5),grid
Get a rough estimate of its amplitude and frequency
[highpeaks,locs] = findpeaks(p5);
lowpeaks = -findpeaks(-p5);
amplitudeEstimate = (mean(highpeaks)-mean(lowpeaks))/2;
frequencyEstimate = 1./mean(diff(t(locs)));
The data is not equally spaced, but we'll assume it is for a first cut and use the mean dt to define a sampling frequency. Not necessarily a recommended approach, but may be good enough for a first cut because the deviation is not too large.
plot(1./diff(t))
Fs = 1/mean(diff(t));
Compute the FFT
P5 = fft(p5-mean(p5))/numel(p5);
f = (0:numel(P5)-1)/numel(P5)*Fs;
If you want to use a window, the scaling has to be modified
whann = hann(numel(p5));
P5hann = fft((p5-mean(p5)).*whann)/sum(whann);
Plot the results
plot(f,2*abs(P5),'-o',f,2*abs(P5hann),'-o'),grid
yline(amplitudeEstimate,'m'),xline(frequencyEstimate,'m')
xlim([0 Fs/2]),xlabel('Frequency (Hz)'),ylabel('Amplitude')
legend('Raw','Windowed')
You can probably sharpen this analysis by being more careful in dealing with the non-equally spaced samples, and maybe zero padding the fft's as well.
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