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applying FFT on Pressure drop signals

조회 수: 4 (최근 30일)
Simran Chaudhary
Simran Chaudhary 2020년 12월 6일
답변: Shadaab Siddiqie 2020년 12월 9일
I want to apply FFT on these pressure drop signals i have. The signals are actually voltage signals which are converted to pressure drop with the help of pressure callibration equations. Here's what i've done so far but i feel that something's wrong with this algorithm. can any one please have a look.
Also i have tones of similar data thats needed to be processed in a similar fashion. now i know a 'for' loop can solve the problem but coding is not my strong suit. please help!!
Additionally, why do we use 2^n in FFT?
[D] = readtable('test_data_19-09-18_1137_0001.xlsx'); %inputting a series of data ex. like an array [1,2,3,4,5]
% [D] = readtable('virgin_data/test_data_19-09-17_1615_0001.xlsx');
% [next_D] = readtable('test_data_19-09-18_1143_0001.xlsx');
D.Voltage_1 = 1380.1*D.Voltage_1 - 1393.8; %PT010-1
D.Voltage_2 = 1372.2*D.Voltage_2 - 1372; %PT053-2
D.Voltage_3 = 1393.2*D.Voltage_3 - 1389.8; %PT051-3
D.Voltage_4 = 1373.6*D.Voltage_4 - 1385.3; %PT050-4
D.Voltage_5 = 1375.3*D.Voltage_5 - 1398.9; %PT062-5
D.Voltage_6 = 1387.5*D.Voltage_6 - 1434.9; %PT061-6
D.Voltage_7 = 1375.3*D.Voltage_7 - 1394.9; %PT005-7
Accel = D(:,2:8);
disp(class(Accel.Voltage_2)); %Displays data type in command window.
Accel.Voltage_1 = Accel.Voltage_1 / double(512);
Accel.Voltage_2 = Accel.Voltage_2 / double(512);
Accel.Voltage_3 = Accel.Voltage_3 / double(512);
Accel.Voltage_4 = Accel.Voltage_4 / double(512);
Accel.Voltage_5 = Accel.Voltage_5 / double(512); % why 512
Accel.Voltage_6 = Accel.Voltage_6 / double(512);
Accel.Voltage_7 = Accel.Voltage_7 / double(512);
% Accel = Accel./ 123;
Ts = 45 ; % Sampling Interval (Number of samples)
Fs = 1000; % Sampling Frequency (The actual number of samples)
Fn = Fs/2; % Nyquist Frequency (Sampling frequency, your sampling frequency must be atleast 2x the frequency of your data
L = size(D,1);
FT_Accel = fft(Accel.Voltage_1-mean(Accel.Voltage_1))/L; % Subtrace Constant Offset & Calculate Fourier Transform
Fv = linspace(0, 1, fix(L/2)+1)*Fn; % Frequency Vector (This is how your graph will display intervals, defines what can possibly land on x or y axis) Similar to resolution of you graph
Iv = 1:numel(Fv); % Index Vector (index will go through data one at a time, its defined so MATLAB knows how to iterate through your data defined by your .xlsx
figure
subplot(3,1,1) %Display Graph
plot(Fv, abs(FT_Accel(Iv,1))*2) %Plot of graph

답변 (2개)

Mathieu NOE
Mathieu NOE 2020년 12월 8일
hello
FYI, this is my prefered fft subfunction ,
function [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
%FFT peak spectrum of signal (example sinus amplitude 1 = 0 dB after fft).
% signal - input signal,
% Fs - Sampling frequency (Hz).
% nfft - FFT window size
% Overlap - buffer overlap % (between 0 and 0.95)
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;
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

Shadaab Siddiqie
Shadaab Siddiqie 2020년 12월 9일
Here is a FFT resource which might help. Coming to "why we use 2^n in FFT" in MATLAB here is a similar question which might help you.

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