Your original ac celeration data have a trend, and you are integrating the trend. Use the detrend function tto remove it (alternatively, use a highpass filter), and the data appears to be much more reasonable.
Try this —
files = dir('*.fig');
for k = 1:numel(files)
fn = files(k).name
F{k} = openfig(fn);
hl{k} = findobj(F{k}, 'Type','line');
x{k,:} = hl{k}.XData;
y{k,:} = hl{k}.YData;
B = [x{k}; ones(size(x{k}))].' \ y{k}.' % Detect Linear Trend
end
format longG
xstats = [mean(diff(x{1})); std(diff(x{1}))]
format short
[FTs1,Fv] = FFT1(y{1},x{1});
figure
plot(Fv, abs(FTs1)*2)
grid
xlabel('Frequency')
ylabel('Magnitude')
xlim([0 1E+3])
ydt = detrend(y{1}, 1);
vel = cumtrapz(x{1}, ydt);
dspl = cumtrapz(x{1}, vel);
figure
plot(x{1}, vel, 'DisplayName','Velocity')
hold on
plot(x{1}, dspl, 'DisplayName','Displacement')
hold off
grid
xlabel('Time')
ylabel('Amplitude')
legend('Location','best')
function [FTs1,Fv] = FFT1(s,t)
t = t(:);
L = numel(t);
if size(s,2) == L
s = s.';
end
Fs = 1/mean(diff(t));
Fn = Fs/2;
NFFT = 2^nextpow2(L);
FTs = fft((s - mean(s)) .* hann(L).*ones(1,size(s,2)), NFFT)/sum(hann(L));
Fv = linspace(0, 1, NFFT/2+1)*Fn;
Iv = 1:numel(Fv);
FTs1 = FTs(Iv,:);
end
There is a small amount of noise in the data, however it does not appear to affect the result.
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