Update the analysis
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figs/asd_and_tf_compare.png
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figs/asd_velocity.png
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figs/compare_psd_original_simulink.png
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figs/compare_psd_tf_technique.png
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figs/cps_integral_comp.png
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figs/psd_velocity.png
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figs/psd_velocity_displacement.png
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figs/psd_velocity_lti_method.png
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figs/time_domain_u.png
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figs/time_domain_x_zoom.png
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1782
index.html
107
matlab/approximate_psd_ifft.m
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@ -0,0 +1,107 @@
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||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
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s = zpk('s');
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% Signal's PSD
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% We load the PSD of the signal we wish to replicate.
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load('./mat/dist_psd.mat', 'dist_f');
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% We remove the first value with very high PSD.
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dist_f.f = dist_f.f(3:end);
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dist_f.psd_gm = dist_f.psd_gm(3:end);
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% The PSD of the signal is shown on figure ref:fig:psd_original.
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figure;
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hold on;
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plot(dist_f.f, dist_f.psd_gm)
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hold off;
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xlabel('Frequency [Hz]');
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ylabel('Power Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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xlim([0.1, 500]);
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% Algorithm
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% We define some parameters that will be used in the algorithm.
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Fs = 2*dist_f.f(end); % Sampling Frequency of data is twice the maximum frequency of the PSD vector [Hz]
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N = 2*length(dist_f.f); % Number of Samples match the one of the wanted PSD
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T0 = N/Fs; % Signal Duration [s]
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df = 1/T0; % Frequency resolution of the DFT [Hz]
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% Also equal to (dist_f.f(2)-dist_f.f(1))
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% We then specify the wanted PSD.
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phi = dist_f.psd_gm;
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% We create amplitudes corresponding to wanted PSD.
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C = zeros(N/2,1);
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for i = 1:N/2
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C(i) = sqrt(phi(i)*df);
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end
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% Finally, we add some random phase to =C=.
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theta = 2*pi*rand(N/2,1); % Generate random phase [rad]
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Cx = [0 ; C.*complex(cos(theta),sin(theta))];
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Cx = [Cx; flipud(conj(Cx(2:end)))];;
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% Obtained Time Domain Signal
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% The time domain data is generated by an inverse FFT.
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% The =ifft= Matlab does not take into account the sampling frequency, thus we need to normalize the signal.
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u = N/sqrt(2)*ifft(Cx); % Normalisation of the IFFT
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t = linspace(0, T0, N+1); % Time Vector [s]
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figure;
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plot(t, u)
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xlabel('Time [s]');
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ylabel('Amplitude');
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xlim([t(1), t(end)]);
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% PSD Comparison
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% We duplicate the time domain signal to have a longer signal and thus a more precise PSD result.
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u_rep = repmat(u, 10, 1);
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% We compute the PSD of the obtained signal with the following commands.
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nx = length(u_rep);
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na = 16;
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win = hanning(floor(nx/na));
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[pxx, f] = pwelch(u_rep, win, 0, [], Fs);
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% Finally, we compare the PSD of the original signal and the obtained signal on figure ref:fig:psd_comparison.
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figure;
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hold on;
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plot(dist_f.f, dist_f.psd_gm, 'DisplayName', 'Original PSD')
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plot(f, pxx, 'DisplayName', 'Computed')
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hold off;
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xlabel('Frequency [Hz]');
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ylabel('Power Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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legend('location', 'northeast');
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xlim([0.1, 500]);
|
150
matlab/approximate_psd_tf.m
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@ -0,0 +1,150 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
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|
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% Signal's PSD
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% We load the PSD of the signal we wish to replicate.
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load('./mat/dist_psd.mat', 'dist_f');
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% We remove the first value with very high PSD.
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dist_f.f = dist_f.f(3:end);
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dist_f.psd_gm = dist_f.psd_gm(3:end);
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% The PSD of the signal is shown on figure ref:fig:psd_ground_motion.
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figure;
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hold on;
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plot(dist_f.f, dist_f.psd_gm)
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hold off;
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xlabel('Frequency [Hz]');
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ylabel('Power Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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xlim([0.1, 500]);
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% Transfer Function that approximate the ASD
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% Using =sisotool= or any other tool, we create a transfer function $G$ such that its magnitude is close to the Amplitude Spectral Density $\Gamma_x = \sqrt{S_x}$:
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% \[ |G(j\omega)| \approx \Gamma_x(\omega) \]
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G_gm = 0.002*(s^2 + 3.169*s + 27.74)/(s*(s+32.73)*(s+8.829)*(s+7.983)^2);
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% We compare the ASD $\Gamma_x(\omega)$ and the magnitude of the generated transfer function $|G(j\omega)|$ in figure [[]].
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figure;
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hold on;
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plot(dist_f.f, sqrt(dist_f.psd_gm))
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plot(dist_f.f, abs(squeeze(freqresp(G_gm, dist_f.f, 'Hz'))))
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hold off;
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xlabel('Frequency [Hz]');
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ylabel('Amplitude Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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xlim([0.1, 500]);
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% Generated Time domain signal
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% We know that a signal $u$ going through a LTI system $G$ (figure [[fig:velocity_to_voltage_psd_bis]]) will have its ASD modified according to the following equation:
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% \begin{equation}
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% \Gamma_{yy}(\omega) = \left|G(j\omega)\right| \Gamma_{uu}(\omega)
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% \end{equation}
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% #+NAME: fig:velocity_to_voltage_psd_bis
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% #+CAPTION: Schematic of the instrumentation used for the measurement
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% [[file:figs/velocity_to_voltage_psd.png]]
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% Thus, if we create a random signal with an ASD equal to one at all frequency and we pass this signal through the previously defined LTI transfer function =G_gm=, we should obtain a signal with an ASD that approximate the original ASD.
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% To obtain a random signal with an ASD equal to one, we use the following code.
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Fs = 2*dist_f.f(end); % Sampling Frequency [Hz]
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Ts = 1/Fs; % Sampling Time [s]
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t = 0:Ts:500; % Time Vector [s]
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u = sqrt(Fs/2)*randn(length(t), 1); % Signal with an ASD equal to one
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% We then use =lsim= to compute $y$ as shown in figure [[fig:velocity_to_voltage_psd_bis]].
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y = lsim(G_gm, u, t);
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% The obtained time domain signal is shown in figure [[fig:time_domain_u]].
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figure;
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plot(t, y);
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xlabel('Time [s]');
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ylabel('Amplitude');
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% Comparison of the Power Spectral Densities
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% We now compute the Power Spectral Density of the computed time domain signal $y$.
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nx = length(y);
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na = 16;
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win = hanning(floor(nx/na));
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[pxx, f] = pwelch(y, win, 0, [], Fs);
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% Finally, we compare the PSD of the original signal and the obtained signal on figure ref:fig:psd_comparison.
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figure;
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hold on;
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||||
plot(dist_f.f, dist_f.psd_gm, 'DisplayName', 'Original PSD')
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plot(f, pxx, 'DisplayName', 'Computed')
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hold off;
|
||||
xlabel('Frequency [Hz]');
|
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ylabel('Power Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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legend('location', 'northeast');
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xlim([0.1, 500]);
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% Simulink
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% One advantage of this technique is that it can be easily integrated into simulink.
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% The corresponding schematic is shown in figure [[fig:simulink_psd_generate]] where the block =Band-Limited White Noise= is used to generate a random signal with a PSD equal to one (parameter =Noise Power= is set to 1).
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% Then, the signal generated pass through the transfer function representing the wanted ASD.
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% #+name: fig:simulink_psd_generate
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% #+caption: Simulink Schematic
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% [[file:figs/simulink_psd_generate.png]]
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% We simulate the system shown in figure [[fig:simulink_psd_generate]].
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out = sim('matlab/generate_signal_psd.slx');
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% And we compute the PSD of the generated signal.
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nx = length(out.u_gm.Data);
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na = 8;
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win = hanning(floor(nx/na));
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[pxx, f] = pwelch(out.u_gm.Data, win, 0, [], 1e3);
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% Finally, we compare the PSD of the generated signal with the original PSD in figure [[fig:compare_psd_original_simulink]].
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figure;
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hold on;
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plot(dist_f.f, dist_f.psd_gm, 'DisplayName', 'Original PSD')
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plot(f, pxx, 'DisplayName', 'Computed from Simulink')
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hold off;
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xlabel('Frequency [Hz]');
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ylabel('Power Spectral Density');
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set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
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||||
legend('location', 'northeast');
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xlim([0.1, 500]);
|
93
matlab/compute_psd_levels.m
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@ -0,0 +1,93 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
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s = zpk('s');
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% Time Domain Signal
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% Let's first define the number of sample and the sampling time.
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N = 10000; % Number of Sample
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dt = 0.001; % Sampling Time [s]
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t = dt*(0:1:N-1)'; % Time vector [s]
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% We generate of signal that consist of:
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% - a white noise with an RMS value equal to =anoi=
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% - two sinusoidal signals
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% The parameters are defined below.
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asig = 0.1; % Amplitude of the signal [V]
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fsig = 10; % Frequency of the signal [Hz]
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ahar = 0.5; % Amplitude of the harmonic [V]
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fhar = 50; % Frequency of the harmonic [Hz]
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anoi = 1e-3; % RMS value of the noise
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% The signal $x$ is generated with the following code and is shown in figure [[fig:time_domain_x_zoom]].
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x = anoi*randn(N, 1) + asig*sin((2*pi*fsig)*t) + ahar*sin((2*pi*fhar)*t);
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figure;
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plot(t, x);
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xlabel('Time [s]');
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ylabel('Amplitude');
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xlim([0, 1]);
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% Estimation of the magnitude of a deterministic signal
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% Let's compute the PSD of the signal using the =blackmanharris= window.
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nx = length(x);
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na = 8;
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win = blackmanharris(floor(nx/na));
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[pxx, f] = pwelch(x, win, 0, [], 1/dt);
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% Normalization of the PSD.
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CG = sum(win)/(nx/na);
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NG = sum(win.^2)/(nx/na);
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fbin = f(2) - f(1);
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pxx_norm = pxx*(NG*fbin/CG^2);
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% We determine the frequency bins corresponding to the frequency of the signals.
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isig = round(fsig/fbin)+1;
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ihar = round(fhar/fbin)+1;
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% The theoretical RMS value of the signal is:
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srmt = asig/sqrt(2); % Theoretical value of signal magnitude
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% And we estimate the RMS value of the signal by either integrating the PSD around the frequency of the signal or by just taking the maximum value.
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srms = sqrt(sum(pxx(isig-5:isig+5)*fbin)); % Signal spectrum integrated
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srmsp = sqrt(pxx_norm(isig) * NG*fbin/CG^2); % Maximum read off spectrum
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% Estimation of the noise level
|
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% The noise level can also be computed using the integration method.
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% The theoretical RMS noise value is.
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nth = anoi/sqrt(max(f)) % Theoretical value [V/sqrt(Hz)]
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|
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% We can estimate this RMS value by integrating the PSD at frequencies where the power of the noise signal is above the power of the other signals.
|
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|
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navg = sqrt(mean(pxx_norm([ihar+10:end]))) % pwelch output averaged
|
279
matlab/spectral_analysis_basics.m
Normal file
@ -0,0 +1,279 @@
|
||||
%% Clear Workspace and Close figures
|
||||
clear; close all; clc;
|
||||
|
||||
%% Intialize Laplace variable
|
||||
s = zpk('s');
|
||||
|
||||
|
||||
|
||||
% #+RESULTS:
|
||||
% [[file:figs/velocity_to_voltage.png]]
|
||||
|
||||
% #+NAME: fig:velocity_to_voltage
|
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% #+CAPTION: Schematic of the instrumentation used for the measurement
|
||||
% #+RESULTS: fig:velocity_to_voltage
|
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% [[file:figs/velocity_to_voltage.png]]
|
||||
|
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% Let's say, we know that the sensitivity of the geophone used is
|
||||
% \[ G_g(s) = G_0 \frac{\frac{s}{2\pi f_0}}{1 + \frac{s}{2\pi f_0}} \quad \left[\frac{V}{m/s}\right] \]
|
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|
||||
% The parameters are defined below.
|
||||
|
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G0 = 88; % Sensitivity [V/(m/s)]
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f0 = 2; % Cut-off frequency [Hz]
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Gg = G0*(s/2/pi/f0)/(1+s/2/pi/f0);
|
||||
|
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|
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|
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% And the dynamics of the amplifier in the bandwidth of interest is just a gain: $G_a(s) = 1000$.
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Ga = 1000;
|
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|
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|
||||
|
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% #+NAME: fig:voltage_to_velocity
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||||
% #+CAPTION: Schematic of the instrumentation used for the measurement
|
||||
% #+RESULTS: fig:voltage_to_velocity
|
||||
% [[file:figs/voltage_to_velocity.png]]
|
||||
|
||||
|
||||
% Let's load the measured $x$.
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|
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data = load('mat/data_028.mat', 'data'); data = data.data;
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t = data(:, 3); % Time vector [s]
|
||||
x = data(:, 1)-mean(data(:, 1)); % The offset if removed (coming from the voltage amplifier) [v]
|
||||
|
||||
dt = t(2)-t(1); % Sampling Time [s]
|
||||
Fs = 1/dt; % Sampling Frequency [Hz]
|
||||
|
||||
|
||||
|
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% We simulate this system with matlab using the =lsim= command.
|
||||
|
||||
v = lsim(inv(Gg*Ga), x, t);
|
||||
|
||||
|
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|
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% And we plot the obtained velocity
|
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figure;
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plot(t, v);
|
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xlabel("Time [s]");
|
||||
ylabel("Velocity [m/s]");
|
||||
|
||||
% Power Spectral Density and Amplitude Spectral Density
|
||||
% From the Matlab documentation:
|
||||
% #+begin_quote
|
||||
% The goal of spectral estimation is to describe the distribution (over frequency) of the power contained in a signal, based on a finite set of data.
|
||||
% #+end_quote
|
||||
|
||||
% We now have the velocity $v$ in the time domain:
|
||||
% \[ v(t)\ [m/s] \]
|
||||
|
||||
% The Power Spectral Density (PSD) $S_v(f)$ of the time domain $v(t)$ can be computed using the following equation:
|
||||
% \[ S_v(f) = \frac{1}{f_s} \sum_{m=-\infty}^{\infty} R_{xx}(m) e^{-j 2 \pi m f / f_s} \ \left[\frac{(m/s)^2}{Hz}\right] \]
|
||||
% where
|
||||
% - $f_s$ is the sampling frequency in Hz
|
||||
% - $R_{xx}$ is the autocorrelation
|
||||
|
||||
% The PSD represents the distribution of the (average) signal power over frequency.
|
||||
% $S_v(f)df$ is the infinitesimal power in the band $(f-df/2, f+df/2)$ and the total power in the signal is obtained by integrating these infinitesimal contributions.
|
||||
|
||||
|
||||
% To compute the Power Spectral Density with matlab, we use the =pwelch= function ([[https://fr.mathworks.com/help/signal/ref/pwelch.html?s_tid=doc_ta][documentation]]).
|
||||
% The use of the =pwelch= function is:
|
||||
% =[pxx,w] = pwelch(x,window,noverlap,nfft, fs)=
|
||||
% With:
|
||||
% - =x= is the discrete time signal
|
||||
% - =window= is a window that is used to smooth the obtained PSD
|
||||
% - =overlap= can be used to have some overlap from section to section
|
||||
% - =nfft= specifies the number of FFT points for the PSD
|
||||
% - =fs= is the sampling frequency of the data =x= in Hertz
|
||||
|
||||
% As explained in cite:schmid12_how_to_use_fft_matlab, it is recommended to use the =pwelch= function the following way:
|
||||
|
||||
% First, define a window (preferably the =hanning= one) by specifying the averaging factor =na=.
|
||||
|
||||
nx = length(v);
|
||||
na = 8;
|
||||
win = hanning(floor(nx/na));
|
||||
|
||||
|
||||
|
||||
% Then, compute the power spectral density $S_v$ and the associated frequency vector $f$ with no overlap.
|
||||
|
||||
[Sv, f] = pwelch(v, win, 0, [], Fs);
|
||||
|
||||
|
||||
|
||||
% The obtained PSD is shown in figure [[fig:psd_velocity]].
|
||||
|
||||
|
||||
figure;
|
||||
plot(f, Sv);
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]');
|
||||
ylabel('Power Spectral Density $\left[\frac{(m/s)^2}{Hz}\right]$')
|
||||
xlim([0.1, 500]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:psd_velocity
|
||||
% #+CAPTION: Power Spectral Density of the measured velocity
|
||||
% #+RESULTS: fig:psd_velocity
|
||||
% [[file:figs/psd_velocity.png]]
|
||||
|
||||
% The Amplitude Spectral Density (ASD) is defined as the square root of the Power Spectral Density and is shown in figure [[fig:asd_velocity]].
|
||||
% \begin{equation}
|
||||
% \Gamma_{vv}(f) = \sqrt{S_{vv}(f)} \quad \left[ \frac{m/s}{\sqrt{Hz}} \right]
|
||||
% \end{equation}
|
||||
|
||||
|
||||
figure;
|
||||
plot(f, sqrt(Sv));
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]');
|
||||
ylabel('Amplitude Spectral Density $\left[\frac{m/s}{\sqrt{Hz}}\right]$')
|
||||
xlim([0.1, 500]);
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:velocity_to_voltage_psd
|
||||
% #+CAPTION: Schematic of the instrumentation used for the measurement
|
||||
% #+RESULTS: fig:velocity_to_voltage_psd
|
||||
% [[file:figs/velocity_to_voltage_psd.png]]
|
||||
|
||||
% Similarly, the ASD of $y$ is:
|
||||
% \begin{equation}
|
||||
% \Gamma_{yy}(\omega) = \left|G(j\omega)\right| \Gamma_{uu}(\omega)
|
||||
% \end{equation}
|
||||
|
||||
% Thus, we could have computed the PSD of $x$ and then obtain the PSD of the velocity with:
|
||||
% \[ S_{v}(\omega) = |G_a(j\omega) G_g(j\omega)|^{-1} S_{x}(\omega) \]
|
||||
|
||||
% The PSD of $x$ is computed below.
|
||||
|
||||
nx = length(x);
|
||||
na = 8;
|
||||
win = hanning(floor(nx/na));
|
||||
[Sx, f] = pwelch(x, win, 0, [], Fs);
|
||||
|
||||
|
||||
|
||||
% And the PSD of $v$ is obtained with the below code.
|
||||
|
||||
Svv = Sx.*squeeze(abs(freqresp(inv(Gg*Ga), f, 'Hz'))).^2;
|
||||
|
||||
|
||||
|
||||
% The result is compare with the PSD computed from the $v$ signal obtained with the =lsim= command in figure [[fig:psd_velocity_lti_method]].
|
||||
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(f, Sv, 'DisplayName', 'lsim technique');
|
||||
plot(f, Svv, 'DisplayName', 'LTI technique');
|
||||
hold off;
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]');
|
||||
ylabel('Power Spectral Density $\left[\frac{(m/s)^2}{Hz}\right]$');
|
||||
xlim([0.1, 500]);
|
||||
legend('location', 'southwest');
|
||||
|
||||
|
||||
|
||||
% #+NAME: fig:velocity_to_displacement_psd
|
||||
% #+CAPTION: Schematic of the instrumentation used for the measurement
|
||||
% #+RESULTS: fig:velocity_to_displacement_psd
|
||||
% [[file:figs/velocity_to_displacement_psd.png]]
|
||||
|
||||
% We then have the relation between the PSD of $d$ and the PSD of $v$:
|
||||
% \begin{equation}
|
||||
% S_{dd}(\omega) = \left|\frac{1}{j \omega}\right|^2 S_{vv}(\omega)
|
||||
% \end{equation}
|
||||
|
||||
% Using a frequency variable $f$ in Hz:
|
||||
% \begin{equation}
|
||||
% S_{dd}(f) = \left| \frac{1}{j 2\pi f} \right|^2 S_{vv}(f)
|
||||
% \end{equation}
|
||||
|
||||
% For the Amplitude Spectral Density:
|
||||
% \begin{equation}
|
||||
% \Gamma_{dd}(f) = \frac{1}{2\pi f} \Gamma_{vv}(f)
|
||||
% \end{equation}
|
||||
|
||||
% Note here that the PSD (and ASD) of one variable and its derivatives/integrals are equal at one particular frequency $f = 1\ rad/s \approx 0.16\ Hz$:
|
||||
% \begin{equation}
|
||||
% S_{xx}(\omega = 1) = S_{vv}(\omega = 1)
|
||||
% \end{equation}
|
||||
|
||||
|
||||
% With Matlab, the PSD of the displacement can be computed from the PSD of the velocity with the following code.
|
||||
|
||||
Sd = Sv.*(1./(2*pi*f)).^2;
|
||||
|
||||
|
||||
|
||||
% The obtained PSD of the displacement can be seen in figure [[fig:psd_velocity_displacement]].
|
||||
|
||||
figure;
|
||||
hold on;
|
||||
plot(f, Sd, 'DisplayName', '$S_d$ in $\left[\frac{m^2}{Hz}\right]$');
|
||||
plot(f, Sv, 'DisplayName', '$S_v$ in $\left[\frac{(m/s)^2}{Hz}\right]$');
|
||||
hold off;
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]');
|
||||
ylabel('Power Spectral Density');
|
||||
xlim([0.1, 500]);
|
||||
legend('location', 'southwest');
|
||||
|
||||
% Cumulative Power/Amplitude Spectrum
|
||||
% The Cumulative Power Spectrum is the cumulative integral of the Power Spectral Density:
|
||||
% #+NAME: eq:cps
|
||||
% \begin{equation}
|
||||
% CPS_v(f) = \int_0^f PSD_v(\nu) d\nu \quad [(m/s)^2]
|
||||
% \end{equation}
|
||||
|
||||
% It is also possible to integrate from high frequency to low frequency:
|
||||
% #+NAME: eq:cps_inv
|
||||
% \begin{equation}
|
||||
% CPS_v(f) = \int_f^\infty PSD_v(\nu) d\nu \quad [(m/s)^2]
|
||||
% \end{equation}
|
||||
|
||||
% The Cumulative Power Spectrum taken at frequency $f$ thus represent the power in the signal in the frequency band $0$ to $f$ or $f$ to $\infty$ depending on the above definition taken.
|
||||
|
||||
|
||||
% The choice of the integral direction depends on the shape of the PSD.
|
||||
% If the power is mostly present at low frequencies, it is preferable to use equation [[eq:cps_inv]].
|
||||
|
||||
|
||||
% The Cumulative Amplitude Spectrum is defined as the square root of the Cumulative Power Spectrum:
|
||||
% \[ CAS_v(f) = \sqrt{CPS_v(f)} = \sqrt{\int_f^\infty PSD_v(\nu) d\nu} \quad [m/s] \]
|
||||
|
||||
% The Root Mean Square value of the velocity corresponds to the Cumulative Amplitude Spectrum when integrated at all frequencies:
|
||||
% \[ v_{\text{rms}} = \sqrt{\int_0^\infty PSD_v(\nu) d\nu} = CAS_v(0) \quad [m/s \ \text{rms}] \]
|
||||
|
||||
% With Matlab, the Cumulative Power Spectrum can be computed with the below formulas and the results are shown in figure [[fig:cps_integral_comp]].
|
||||
|
||||
CPS_v = cumtrapz(f, Sv); % Cumulative Power Spectrum from low to high frequencies
|
||||
CPS_vv = flip(-cumtrapz(flip(f), flip(Sv))); % Cumulative Power Spectrum from high to low frequencies
|
||||
|
||||
figure;
|
||||
ax1 = subplot(1, 2, 1);
|
||||
hold on;
|
||||
plot(f, CPS_v);
|
||||
hold off;
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]'); ylabel('Cumulative Power Spectrum [$(m/s)^2$]')
|
||||
|
||||
ax2 = subplot(1, 2, 2);
|
||||
hold on;
|
||||
plot(f, CPS_vv);
|
||||
hold off;
|
||||
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
|
||||
xlabel('Frequency [Hz]');
|
||||
|
||||
linkaxes([ax1,ax2],'xy');
|
||||
xlim([0.1, 500]); ylim([1e-15, 1e-11]);
|
8
ref.bib
@ -17,4 +17,12 @@
|
||||
doi = {10.1007/978-94-017-2840-9},
|
||||
pages = {nil},
|
||||
series = {Solid Mechanics and Its Applications},
|
||||
}
|
||||
|
||||
@book{stoica05_spect,
|
||||
author = {Stoica, Petre and Moses, Randolph L and others},
|
||||
title = {Spectral analysis of signals},
|
||||
year = 2005,
|
||||
publisher = {Pearson Prentice Hall Upper Saddle River, NJ},
|
||||
keywords = {},
|
||||
}
|