fourier Transformation of the signal from a motor

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I have taken vibration signal from a electric motor. But I am not sure the my codes that are correct or not. file is attached in here. please tell me is it correct or not?
clc
clear
[y, Fs]=audioread('09_52_00.wav');
n=length(y);
t=(0:n-1)/Fs;
plot(t,y);
xlabel('Time');
ylabel('Audio signal');
title('Original signal');
figure(2)
nfft=length(y); %length of time domain signal
nfft2=2^nextpow2(nfft);%length of signal in power of 2
ff=fft(y,nfft2);
abs_s=abs(ff);
freq=0:(1/t(end)):Fs/2-(1/t(end));
plot(freq,abs_s(1:length(freq)))

Accepted Answer

Mathieu NOE
Mathieu NOE on 22 Aug 2022
hello
this is my suggestion for your application (the fft is done one multiple data chuncks of NFFT length with overlapping , then fft and linear averaging ; this improves the signal to noise ratio).
here I opted for NFFT = 2000 (= Fs) , so the frequency resolution is 1 Hz which is good enough to see the dominating tone at 100 Hz (twice the 50 Hz mains frequency) + others frequencies. You can of course try other settings to see the effects.
clc
clearvars
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 2000; % then df = 1 Hz
OVERLAP = 0.75;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[signal, Fs]=audioread('09_52_00.wav');
dt = 1/Fs;
[samples,channels] = size(signal);
% time vector
time = (0:samples-1)*dt;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 1 : time domain plot
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
figure(1),
plot(time,signal);grid on
title(['Time plot / Fs = ' num2str(Fs) ' Hz ']);
xlabel('Time (s)');ylabel('Amplitude');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display 2 : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[freq, spectrum_raw] = myfft_peak(signal,Fs,NFFT,OVERLAP);
figure(2),plot(freq,20*log10(spectrum_raw));grid on
df = freq(2)-freq(1); % frequency resolution
title(['Averaged FFT Spectrum / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(df,3) ' Hz ']);
xlabel('Frequency (Hz)');ylabel('Amplitude (dB)');
function [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal (example sinus amplitude 1 = 0 dB after fft).
% Linear averaging
% signal - input signal,
% Fs - Sampling frequency (Hz).
% nfft - FFT window size
% Overlap - buffer percentage of overlap % (between 0 and 0.95)
[samples,channels] = size(signal);
% fill signal with zeros if its length is lower than nfft
if samples<nfft
s_tmp = zeros(nfft,channels);
s_tmp((1:samples),:) = signal;
signal = s_tmp;
samples = nfft;
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*ones(1,channels));
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

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