fourier Transformation of the signal from a motor

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)))

Answer 1

[Mathieu NOE]

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