28 KiB
28 KiB
- Effect of the Slip-Ring on the signal
- Effect of all the control systems on the Sample vibrations
- Transfer function from one stage to the other
#+TITLE:Measurements
For all the measurements shown here:
- geophones used are L22 with a resonance frequency of 1Hz
- the signals are amplified with voltage amplifiers with a gain of 60dB
- the voltage amplifiers include a low pass filter with a cut-off frequency at 1kHz
Effect of the Slip-Ring on the signal
Experimental Setup
Two measurements are made with the control systems of all the stages turned OFF.
One geophone is located on the marble while the other is located at the sample location (figure fig:setup_slipring).
The two measurements are:
| Measurement File | Description |
|---|---|
meas_008.mat |
Signal from the top geophone does not goes through the Slip-ring |
meas_009.mat |
Signal goes through the Slip-ring (as shown on the figure above) |
Each of the measurement mat file contains one data array with 3 columns:
| Column number | Description |
|---|---|
| 1 | Geophone - Marble |
| 2 | Geophone - Sample |
| 3 | Time |
Load data
We load the data of the z axis of two geophones.
d8 = load('mat/data_008.mat', 'data'); d8 = d8.data;
d9 = load('mat/data_009.mat', 'data'); d9 = d9.data;Analysis - Time Domain
First, we compare the time domain signals for the two experiments (figure fig:slipring_time).
figure;
hold on;
plot(d9(:, 3), d9(:, 2), 'DisplayName', 'Slip-Ring');
plot(d8(:, 3), d8(:, 2), 'DisplayName', 'Wire');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
xlim([0, 50]);
legend('location', 'northeast'); <<plt-matlab>>
Analysis - Frequency Domain
We then compute the Power Spectral Density of the two signals and we compare them (figure fig:slipring_asd).
dt = d8(2, 3) - d8(1, 3);
Fs = 1/dt;
win = hanning(ceil(1*Fs)); [pxx8, f] = pwelch(d8(:, 2), win, [], [], Fs);
[pxx9, ~] = pwelch(d9(:, 2), win, [], [], Fs); figure;
hold on;
plot(f, sqrt(pxx9), 'DisplayName', 'Slip-Ring');
plot(f, sqrt(pxx8), 'DisplayName', 'Wire');
hold off;
set(gca, 'xscale', 'log');
set(gca, 'yscale', 'log');
xlabel('Frequency [Hz]'); ylabel('ASD [V/sqrt(Hz)]')
xlim([1, 500]);
legend('Location', 'southwest'); <<plt-matlab>>
Conclusion
- Connecting the geophone through the Slip-Ring seems to induce a lot of noise.
Remaining questions to answer:
- Why is there a sharp peak at 300Hz?
- Why the use of the Slip-Ring does induce a noise?
- Can the capacitive/inductive properties of the wires in the Slip-ring does not play well with the geophone? (resonant RLC circuit)
Effect of all the control systems on the Sample vibrations
Experimental Setup
We here measure the signals of two geophones:
- One is located on top of the Sample platform
- One is located on the marble
The signal from the top geophone does not go trought the slip-ring.
First, all the control systems are turned ON, then, they are turned one by one. Each measurement are done during 50s.
| Ty | Ry | Slip Ring | Spindle | Hexapod | Meas. file |
|---|---|---|---|---|---|
| ON | ON | ON | ON | ON | meas_003.mat |
| OFF | ON | ON | ON | ON | meas_004.mat |
| OFF | OFF | ON | ON | ON | meas_005.mat |
| OFF | OFF | OFF | ON | ON | meas_006.mat |
| OFF | OFF | OFF | OFF | ON | meas_007.mat |
| OFF | OFF | OFF | OFF | OFF | meas_008.mat |
Each of the mat file contains one array data with 3 columns:
| Column number | Description |
|---|---|
| 1 | Geophone - Marble |
| 2 | Geophone - Sample |
| 3 | Time |
Load data
We load the data of the z axis of two geophones.
d3 = load('mat/data_003.mat', 'data'); d3 = d3.data;
d4 = load('mat/data_004.mat', 'data'); d4 = d4.data;
d5 = load('mat/data_005.mat', 'data'); d5 = d5.data;
d6 = load('mat/data_006.mat', 'data'); d6 = d6.data;
d7 = load('mat/data_007.mat', 'data'); d7 = d7.data;
d8 = load('mat/data_008.mat', 'data'); d8 = d8.data;Analysis - Time Domain
First, we can look at the time domain data and compare all the measurements:
- comparison for the geophone at the sample location (figure fig:time_domain_sample)
- comparison for the geophone on the granite (figure fig:time_domain_marble)
figure;
hold on;
plot(d3(:, 3), d3(:, 2), 'DisplayName', 'All ON');
plot(d4(:, 3), d4(:, 2), 'DisplayName', 'Ty OFF');
plot(d5(:, 3), d5(:, 2), 'DisplayName', 'Ry OFF');
plot(d6(:, 3), d6(:, 2), 'DisplayName', 'S-R OFF');
plot(d7(:, 3), d7(:, 2), 'DisplayName', 'Rz OFF');
plot(d8(:, 3), d8(:, 2), 'DisplayName', 'Hexa OFF');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
xlim([0, 50]);
legend('Location', 'bestoutside'); <<plt-matlab>>
figure;
hold on;
plot(d3(:, 3), d3(:, 1), 'DisplayName', 'All ON');
plot(d4(:, 3), d4(:, 1), 'DisplayName', 'Ty OFF');
plot(d5(:, 3), d5(:, 1), 'DisplayName', 'Ry OFF');
plot(d6(:, 3), d6(:, 1), 'DisplayName', 'S-R OFF');
plot(d7(:, 3), d7(:, 1), 'DisplayName', 'Rz OFF');
plot(d8(:, 3), d8(:, 1), 'DisplayName', 'Hexa OFF');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
xlim([0, 50]);
legend('Location', 'bestoutside'); <<plt-matlab>>
Analysis - Frequency Domain
dt = d3(2, 3) - d3(1, 3);
Fs = 1/dt;
win = hanning(ceil(10*Fs));Vibrations at the sample location
First, we compute the Power Spectral Density of the signals coming from the Geophone located at the sample location.
[px3, f] = pwelch(d3(:, 2), win, [], [], Fs);
[px4, ~] = pwelch(d4(:, 2), win, [], [], Fs);
[px5, ~] = pwelch(d5(:, 2), win, [], [], Fs);
[px6, ~] = pwelch(d6(:, 2), win, [], [], Fs);
[px7, ~] = pwelch(d7(:, 2), win, [], [], Fs);
[px8, ~] = pwelch(d8(:, 2), win, [], [], Fs);And we compare all the signals (figures fig:psd_sample_comp and fig:psd_sample_comp_high_freq).
figure;
hold on;
plot(f, sqrt(px3), 'DisplayName', 'All ON');
plot(f, sqrt(px4), 'DisplayName', 'Ty OFF');
plot(f, sqrt(px5), 'DisplayName', 'Ry OFF');
plot(f, sqrt(px6), 'DisplayName', 'S-R OFF');
plot(f, sqrt(px7), 'DisplayName', 'Rz OFF');
plot(f, sqrt(px8), 'DisplayName', 'Hexa OFF');
hold off;
set(gca, 'xscale', 'log');
set(gca, 'yscale', 'log');
xlabel('Frequency [Hz]'); ylabel('ASD [V/sqrt(Hz)]')
xlim([0.1, 500]);
legend('Location', 'southwest'); <<plt-matlab>>
<<plt-matlab>>
Vibrations on the marble
Now we plot the same curves for the geophone located on the marble.
[px3, f] = pwelch(d3(:, 1), win, [], [], Fs);
[px4, ~] = pwelch(d4(:, 1), win, [], [], Fs);
[px5, ~] = pwelch(d5(:, 1), win, [], [], Fs);
[px6, ~] = pwelch(d6(:, 1), win, [], [], Fs);
[px7, ~] = pwelch(d7(:, 1), win, [], [], Fs);
[px8, ~] = pwelch(d8(:, 1), win, [], [], Fs);And we compare the ASD (figures fig:psd_marble_comp and fig:psd_marble_comp_high_freq)
figure;
hold on;
plot(f, sqrt(px3), 'DisplayName', 'All ON');
plot(f, sqrt(px4), 'DisplayName', 'Ty OFF');
plot(f, sqrt(px5), 'DisplayName', 'Ry OFF');
plot(f, sqrt(px6), 'DisplayName', 'S-R OFF');
plot(f, sqrt(px7), 'DisplayName', 'Rz OFF');
plot(f, sqrt(px8), 'DisplayName', 'Hexa OFF');
hold off;
set(gca, 'xscale', 'log');
set(gca, 'yscale', 'log');
xlabel('Frequency [Hz]'); ylabel('ASD [V/sqrt(Hz)]')
xlim([0.1, 500]);
legend('Location', 'northeast'); <<plt-matlab>>
<<plt-matlab>>
Effect of the control system on the transmissibility from ground to sample
As the feedback loops change the dynamics of the system, we should see differences on the transfer function from marble velocity to sample velocity when turning off the control systems (figure fig:trans_comp).
dt = d3(2, 3) - d3(1, 3);
Fs = 1/dt;
win = hanning(ceil(1*Fs));First, we compute the Power Spectral Density of the signals coming from the Geophone located at the sample location.
[T3, f] = tfestimate(d3(:, 1), d3(:, 2), win, [], [], Fs);
[T4, ~] = tfestimate(d4(:, 1), d4(:, 2), win, [], [], Fs);
[T5, ~] = tfestimate(d5(:, 1), d5(:, 2), win, [], [], Fs);
[T6, ~] = tfestimate(d6(:, 1), d6(:, 2), win, [], [], Fs);
[T7, ~] = tfestimate(d7(:, 1), d7(:, 2), win, [], [], Fs);
[T8, ~] = tfestimate(d8(:, 1), d8(:, 2), win, [], [], Fs); figure;
ax1 = subplot(2, 1, 1);
hold on;
plot(f, abs(T3), 'DisplayName', 'All ON');
plot(f, abs(T4), 'DisplayName', 'Ty OFF');
plot(f, abs(T5), 'DisplayName', 'Ry OFF');
plot(f, abs(T6), 'DisplayName', 'S-R OFF');
plot(f, abs(T7), 'DisplayName', 'Rz OFF');
plot(f, abs(T8), 'DisplayName', 'Hexa OFF');
hold off;
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
set(gca, 'XTickLabel',[]);
ylabel('Magnitude');
legend('Location', 'northwest');
ax2 = subplot(2, 1, 2);
hold on;
plot(f, mod(180+180/pi*phase(T3), 360)-180);
plot(f, mod(180+180/pi*phase(T4), 360)-180);
plot(f, mod(180+180/pi*phase(T5), 360)-180);
plot(f, mod(180+180/pi*phase(T6), 360)-180);
plot(f, mod(180+180/pi*phase(T7), 360)-180);
plot(f, mod(180+180/pi*phase(T8), 360)-180);
hold off;
set(gca, 'xscale', 'log');
ylim([-180, 180]);
yticks([-180, -90, 0, 90, 180]);
xlabel('Frequency [Hz]'); ylabel('Phase');
linkaxes([ax1,ax2],'x');
xlim([1, 500]); <<plt-matlab>>
Conclusion
- The control system of the Ty stage induces a lot of vibrations of the marble
- Why it seems that the measurement noise at high frequency is the limiting factor when the slip ring is ON but not when it is OFF?
Transfer function from one stage to the other
Experimental Setup
For all the measurements in this section:
- all the control stages are OFF.
- the measurements are on the $z$ direction
From Marble to Ty - mat/meas_010.mat
One geophone is on the marble, one is on the Ty stage (see figures fig:setup_m_ty, fig:setup_m_ty_zoom and fig:setup_m_ty_top).
The data array contains the following columns:
| Column | Description |
|---|---|
| 1 | Ground |
| 2 | Ty |
| 3 | Time |
From Marble to Ry - mat/meas_011.mat
One geophone is on the marble, one is on the Ry stage (see figure fig:setup_m_ry)
The data array contains the following columns:
| Column | Description |
|---|---|
| 1 | Ground |
| 2 | Ry |
| 3 | Time |
From Ty to Ry - mat/meas_012.mat
One geophone is on the Ty stage, one is on the Ry stage (see figures fig:setup_ty_ry, fig:setup_ty_ry_top and fig:setup_ty_ry_zoom) One geophone on the Ty stage, one geophone on the Ry stage.
The data array contains the following columns:
| Column | Description |
|---|---|
| 1 | Ty |
| 2 | Ry |
| 3 | Time |
Load data
We load the data of the z axis of two geophones.
m_ty = load('mat/data_010.mat', 'data'); m_ty = m_ty.data;
m_ry = load('mat/data_011.mat', 'data'); m_ry = m_ry.data;
ty_ry = load('mat/data_012.mat', 'data'); ty_ry = ty_ry.data;Analysis - Time Domain
First, we can look at the time domain data.
figure;
hold on;
plot(m_ty(:, 3), m_ty(:, 1), 'DisplayName', 'Marble');
plot(m_ty(:, 3), m_ty(:, 2), 'DisplayName', 'Ty');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
legend('Location', 'northeast');
xlim([0, 500]); <<plt-matlab>>
figure;
hold on;
plot(m_ry(:, 3), m_ry(:, 1), 'DisplayName', 'Marble');
plot(m_ry(:, 3), m_ry(:, 2), 'DisplayName', 'Ty');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
legend('Location', 'northeast');
xlim([0, 500]); <<plt-matlab>>
figure;
hold on;
plot(ty_ry(:, 3), ty_ry(:, 1), 'DisplayName', 'Ty');
plot(ty_ry(:, 3), ty_ry(:, 2), 'DisplayName', 'Ry');
hold off;
xlabel('Time [s]'); ylabel('Voltage [V]');
legend('Location', 'northeast');
xlim([0, 500]); <<plt-matlab>>
Analysis - Frequency Domain
dt = m_ty(2, 3) - m_ty(1, 3);
Fs = 1/dt;
win = hanning(ceil(1*Fs));First, we compute the transfer function estimate between the two geophones for the 3 experiments (figure fig:compare_tf_geophones). We also plot their coherence (figure fig:coherence_two_geophones).
[T_m_ty, f] = tfestimate(m_ty(:, 1), m_ty(:, 2), win, [], [], Fs);
[T_m_ry, ~] = tfestimate(m_ry(:, 1), m_ry(:, 2), win, [], [], Fs);
[T_ty_ry, ~] = tfestimate(ty_ry(:, 1), ty_ry(:, 2), win, [], [], Fs); figure;
ax1 = subplot(2, 1, 1);
hold on;
plot(f, abs(T_m_ty), 'DisplayName', 'Marble - Ty');
plot(f, abs(T_m_ry), 'DisplayName', 'Marble - Ry');
plot(f, abs(T_ty_ry), 'DisplayName', 'Ty - Ry');
hold off;
set(gca, 'xscale', 'log'); set(gca, 'yscale', 'log');
set(gca, 'XTickLabel',[]);
ylabel('Magnitude');
legend('Location', 'northwest');
ax2 = subplot(2, 1, 2);
hold on;
plot(f, mod(180+180/pi*phase(T_m_ty), 360)-180);
plot(f, mod(180+180/pi*phase(T_m_ry), 360)-180);
plot(f, mod(180+180/pi*phase(T_ty_ry), 360)-180);
hold off;
set(gca, 'xscale', 'log');
ylim([-180, 180]);
yticks([-180, -90, 0, 90, 180]);
xlabel('Frequency [Hz]'); ylabel('Phase');
linkaxes([ax1,ax2],'x');
xlim([10, 500]); <<plt-matlab>>
[coh_m_ty, f] = mscohere(m_ty(:, 1), m_ty(:, 2), win, [], [], Fs);
[coh_m_ry, ~] = mscohere(m_ry(:, 1), m_ry(:, 2), win, [], [], Fs);
[coh_ty_ry, ~] = mscohere(ty_ry(:, 1), ty_ry(:, 2), win, [], [], Fs); <<plt-matlab>>