nass-micro-station-measurements/2018-10-15 - Marc/index.org

Measurement Analysis

• Measurement Description
• Importation of the data
• Variables for analysis
• Coherence between the two vertical geophones on the Tilt Stage
• Data Post Processing
• Normalization
• Measurement 1 - Effect of Ty stage
• Measurement 2 - Effect of Ry stage
• Measurement 3 - Effect of the Hexapod
• Measurement 4 - Effect of the Splip-Ring and Spindle
• Measurement 5 - Transmission from ground to marble

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Measurement Description

Picture of the setup for the measurement

The sensor used are 3 L-4C geophones (Documentation).

Each motor are turn off and then on.

The goal is to see what noise is injected in the system due to the regulation loop of each stage.

Importation of the data

First, load all the measurement files:

  meas = {};
  meas{1} = load('./mat/Measurement1.mat');
  meas{2} = load('./mat/Measurement2.mat');
  meas{3} = load('./mat/Measurement3.mat');
  meas{4} = load('./mat/Measurement4.mat');
  meas{5} = load('./mat/Measurement5.mat');

Change the track name for measurements 3 and 4.

  meas{3}.Track1_Name = 'Input 1: Hexa Z';
  meas{4}.Track1_Name = 'Input 1: Hexa Z';

For the measurements 1 to 4, the measurement channels are shown table tab:meas_14.

	Channel 1	Channel 2	Channel 3
Meas. 1	Input 1: tilt1 Z	Input 2: tilt2 Z	Input 3: Ty Y
Meas. 2	Input 1: tilt1 Z	Input 2: tilt2 Z	Input 3: Ty Y
Meas. 3	Input 1: Hexa Z	Input 2: tilt2 Z	Input 3: Ty Y
Meas. 4	Input 1: Hexa Z	Input 2: tilt2 Z	Input 3: Ty Y

Channels for measurements 1 to 4

For the measurement 5, the channels are shown table tab:meas_5.

	Channel 1	Channel 2	Channel 3	Channel 4
Meas. 5	Input 1: Floor Z	Input 2: Marble Z	Input 3: Floor Y	Input 4: Marble Y

Channels for measurement 5

Variables for analysis

We define the sampling frequency and the time vectors for the plots.

  Fs = 256; % [Hz]
  dt = 1/(Fs);
  t1  = dt*[0:length(meas{1}.Track1)-1];
  t2  = dt*[0:length(meas{2}.Track1)-1];
  t3  = dt*[0:length(meas{3}.Track1)-1];
  t4  = dt*[0:length(meas{4}.Track1)-1];
  t5  = dt*[0:length(meas{5}.Track1)-1];

For the frequency analysis, we define the frequency limits for the plot.

  fmin = 1; % [Hz]
  fmax = 100; % [Hz]

Then we define the windows that will be used to average the results.

  psd_window = hanning(2*fmin/dt);

Coherence between the two vertical geophones on the Tilt Stage

We first compute the coherence between the two geophones located on the tilt stage. The result is shown on figure fig:coherence_vertical_tilt_sensors.

  [coh, f] = mscohere(meas{1}.Track1(:), meas{1}.Track2(:), psd_window, [], [], Fs);

  <<plt-matlab>>

Coherence between the two vertical sensors positionned on the Tilt Stage

We then compute the transfer function from one sensor to the other (figure fig:tf_vertical_tilt_sensors).

  [tf23, f] = tfestimate(meas{1}.Track1(:), meas{1}.Track2(:), psd_window, [], [], Fs);

  <<plt-matlab>>

Transfer function from one vertical geophone on the tilt stage to the other vertical geophone on the tilt stage

Even though the coherence is not very good, we observe no resonance between the two sensors.

Data Post Processing

When using two geophone sensors on the same tilt stage (measurements 1 and 2), we post-process the data to obtain the z displacement and the rotation of the tilt stage:

  meas1_z    = (meas{1}.Track1+meas{1}.Track2)/2;
  meas1_tilt = (meas{1}.Track1-meas{1}.Track2)/2;

  meas{1}.Track1 = meas1_z;
  meas{1}.Track1_Y_Magnitude = 'Meter / second';
  meas{1}.Track1_Name = 'Ry Z';
  meas{1}.Track2 = meas1_tilt;
  meas{1}.Track2_Y_Magnitude = 'Rad / second';
  meas{1}.Track2_Name = 'Ry Tilt';

  meas2_z    = (meas{2}.Track1+meas{2}.Track2)/2;
  meas2_tilt = (meas{2}.Track1-meas{2}.Track2)/2;
  meas{2}.Track1 = meas2_z;
  meas{2}.Track1_Y_Magnitude = 'Meter / second';
  meas{2}.Track1_Name = 'Ry Z';
  meas{2}.Track2 = meas2_tilt;
  meas{2}.Track2_Y_Magnitude = 'Rad / second';
  meas{2}.Track2_Name = 'Ry Tilt';

Normalization

Parameters of the geophone are defined below. The transfer function from geophone velocity to measured voltage is shown on figure fig:L4C_bode_plot.

Measurements will be normalized by the inverse of this transfer function in order to go from voltage measurement to velocity measurement.

  L4C_w0 = 2*pi; % [rad/s]
  L4C_ksi = 0.28;
  L4C_G0 = 276.8; % [V/(m/s)]
  L4C_G = L4C_G0*(s/L4C_w0)^2/((s/L4C_w0)^2 + 2*L4C_ksi*(s/L4C_w0) + 1);

  <<plt-matlab>>

Bode plot of the L4C Geophone

  meas{1}.Track1 = (meas{1}.Track1)./276.8;
  meas{1}.Track2 = (meas{1}.Track2)./276.8;
  meas{1}.Track3 = (meas{1}.Track3)./276.8;

  meas{2}.Track1 = (meas{2}.Track1)./276.8;
  meas{2}.Track2 = (meas{2}.Track2)./276.8;
  meas{2}.Track3 = (meas{2}.Track3)./276.8;

  meas{3}.Track1 = (meas{3}.Track1)./276.8;
  meas{3}.Track2 = (meas{3}.Track2)./276.8;
  meas{3}.Track3 = (meas{3}.Track3)./276.8;

  meas{4}.Track1 = (meas{4}.Track1)./276.8;
  meas{4}.Track2 = (meas{4}.Track2)./276.8;
  meas{4}.Track3 = (meas{4}.Track3)./276.8;

  meas{5}.Track1 = (meas{5}.Track1)./276.8;
  meas{5}.Track2 = (meas{5}.Track2)./276.8;
  meas{5}.Track3 = (meas{5}.Track3)./276.8;
  meas{5}.Track4 = (meas{5}.Track4)./276.8;

  meas{1}.Track1_norm = lsim(inv(L4C_G), meas{1}.Track1, t1);
  meas{1}.Track2_norm = lsim(inv(L4C_G), meas{1}.Track2, t1);
  meas{1}.Track3_norm = lsim(inv(L4C_G), meas{1}.Track3, t1);

  meas{2}.Track1_norm = lsim(inv(L4C_G), meas{2}.Track1, t2);
  meas{2}.Track2_norm = lsim(inv(L4C_G), meas{2}.Track2, t2);
  meas{2}.Track3_norm = lsim(inv(L4C_G), meas{2}.Track3, t2);

  meas{3}.Track1_norm = lsim(inv(L4C_G), meas{3}.Track1, t3);
  meas{3}.Track2_norm = lsim(inv(L4C_G), meas{3}.Track2, t3);
  meas{3}.Track3_norm = lsim(inv(L4C_G), meas{3}.Track3, t3);

  meas{4}.Track1_norm = lsim(inv(L4C_G), meas{4}.Track1, t4);
  meas{4}.Track2_norm = lsim(inv(L4C_G), meas{4}.Track2, t4);
  meas{4}.Track3_norm = lsim(inv(L4C_G), meas{4}.Track3, t4);

  meas{5}.Track1_norm = lsim(inv(L4C_G), meas{5}.Track1, t5);
  meas{5}.Track2_norm = lsim(inv(L4C_G), meas{5}.Track2, t5);
  meas{5}.Track3_norm = lsim(inv(L4C_G), meas{5}.Track3, t5);
  meas{5}.Track4_norm = lsim(inv(L4C_G), meas{5}.Track4, t5);

Measurement 1 - Effect of Ty stage

The configuration for this measurement is shown table tab:conf_meas1.

Time	0-309	309-end
Ty	OFF	ON
Ry	OFF	OFF
SlipRing	OFF	OFF
Spindle	OFF	OFF
Hexa	OFF	OFF

We then plot the measurements in time domain (figure fig:meas1).

We observe strange behavior when the Ty stage is turned on. How can we explain that?

  <<plt-matlab>>

Time domain - measurement 1

To understand what is going on, instead of looking at the velocity, we can look at the displacement by integrating the data. The displacement is computed by integrating the velocity using cumtrapz function.

Then we plot the position with respect to time (figure fig:meas1_disp).

  <<plt-matlab>>

Y displacement of the Ty stage

We when compute the power spectral density of each measurement before and after turning on the stage.

  [pxx111, f11] = pwelch(meas{1}.Track1(1:ceil(300/dt)),   psd_window, [], [], Fs);
  [pxx112, f12] = pwelch(meas{1}.Track1(ceil(350/dt):end), psd_window, [], [], Fs);

  [pxx121, ~] = pwelch(meas{1}.Track2(1:ceil(300/dt)),   psd_window, [], [], Fs);
  [pxx122, ~] = pwelch(meas{1}.Track2(ceil(350/dt):end), psd_window, [], [], Fs);

  [pxx131, ~] = pwelch(meas{1}.Track3(1:ceil(300/dt)),   psd_window, [], [], Fs);
  [pxx132, ~] = pwelch(meas{1}.Track3(ceil(350/dt):end), psd_window, [], [], Fs);

We finally plot the power spectral density of each track (figures fig:meas1_ry_z_psd, fig:meas1_ry_tilt_psd, fig:meas1_ty_y_psd).

  <<plt-matlab>>

PSD of the Z velocity of Ry stage - measurement 1

  <<plt-matlab>>

PSD of the Rotation of Ry Stage - measurement 1

  <<plt-matlab>>

PSD of the Ty velocity in the Y direction - measurement 1

Turning on the Y-translation stage increases the velocity of the Ty stage in the Y direction and the rotation motion of the tilt stage:

• at 20Hz
• at 40Hz
• between 80Hz and 90Hz

It does not seems to have any effect on the Z motion of the tilt stage.

Measurement 2 - Effect of Ry stage

The tilt stage is turned ON at around 326 seconds (table tab:conf_meas2).

Time	0-326	326-end
Ty	OFF	OFF
Ry	OFF	ON
SlipRing	OFF	OFF
Spindle	OFF	OFF
Hexa	OFF	OFF

We plot the time domain (figure fig:meas2) and we don't observe anything special in the time domain.

  <<plt-matlab>>

Time domain - measurement 2

  <<plt-matlab>>

Time domain - measurement 2

We compute the PSD of each track and we plot them (figures fig:meas2_ry_z_psd, fig:meas2_ry_tilt_psd and fig:meas2_ty_y_psd ).

  [pxx211, f21] = pwelch(meas{2}.Track1(1:ceil(326/dt)),   psd_window, [], [], Fs);
  [pxx212, f22] = pwelch(meas{2}.Track1(ceil(326/dt):end), psd_window, [], [], Fs);

  [pxx221, ~] = pwelch(meas{2}.Track2(1:ceil(326/dt)),   psd_window, [], [], Fs);
  [pxx222, ~] = pwelch(meas{2}.Track2(ceil(326/dt):end), psd_window, [], [], Fs);

  [pxx231, ~] = pwelch(meas{2}.Track3(1:ceil(326/dt)),   psd_window, [], [], Fs);
  [pxx232, ~] = pwelch(meas{2}.Track3(ceil(326/dt):end), psd_window, [], [], Fs);

  <<plt-matlab>>

PSD of the Z velocity of Ry Stage - measurement 2

  <<plt-matlab>>

PSD of the Rotation motion of Ry Stage - measurement 2

  <<plt-matlab>>

PSD of the Ty velocity in the Y direction - measurement 2

We observe no noticeable difference when the Tilt-stage is turned ON expect a small decrease of the Z motion of the tilt stage around 10Hz.

Measurement 3 - Effect of the Hexapod

The hexapod is turned off after 406 seconds (table tab:conf_meas3).

Time	0-406	406-end
Ty	OFF	OFF
Ry	ON	ON
SlipRing	OFF	OFF
Spindle	OFF	OFF
Hexa	ON	OFF

The time domain result is shown figure fig:meas3.

  <<plt-matlab>>

Time domain - measurement 3

  <<plt-matlab>>

Time domain - measurement 3

We then compute the PSD of each track before and after turning off the hexapod and plot the results in the figures fig:meas3_hexa_z_psd, fig:meas3_ry_z_psd and fig:meas3_ty_y_psd.

  [pxx311, f31] = pwelch(meas{3}.Track1(1:ceil(400/dt)),   psd_window, [], [], Fs);
  [pxx312, f32] = pwelch(meas{3}.Track1(ceil(420/dt):end), psd_window, [], [], Fs);

  [pxx321, ~] = pwelch(meas{3}.Track2(1:ceil(400/dt)),   psd_window, [], [], Fs);
  [pxx322, ~] = pwelch(meas{3}.Track2(ceil(420/dt):end), psd_window, [], [], Fs);

  [pxx331, ~] = pwelch(meas{3}.Track3(1:ceil(400/dt)),   psd_window, [], [], Fs);
  [pxx332, ~] = pwelch(meas{3}.Track3(ceil(420/dt):end), psd_window, [], [], Fs);

  <<plt-matlab>>

PSD of the Z velocity of the Hexapod - measurement 3

  <<plt-matlab>>

PSD of the Z velocity of the Ry stage - measurement 3

  <<plt-matlab>>

PSD of the Ty velocity in the Y direction - measurement 3

Turning ON induces some motion on the hexapod in the z direction (figure fig:meas3_hexa_z_psd), on the tilt stage in the z direction (figure fig:meas3_ry_z_psd) and on the y motion of the Ty stage (figure fig:meas3_ty_y_psd):

• at 17Hz
• at 34Hz

Measurement 4 - Effect of the Splip-Ring and Spindle

The slip ring is turned on at 300s, then the spindle is turned on at 620s (table tab:conf_meas4). The time domain signals are shown figure fig:meas4.

Time	0-300	300-620	620-end
Ty	OFF	OFF	OFF
Ry	OFF	OFF	OFF
SlipRing	OFF	ON	ON
Spindle	OFF	OFF	ON
Hexa	OFF	OFF	OFF

  <<plt-matlab>>

Time domain - measurement 4

If we integrate this signal, we obtain Figure fig:meas4_int.

  <<plt-matlab>>

Time domain - measurement 4

The PSD of each track are computed using the code below.

  <<plt-matlab>>

PSD of the Z velocity of the Hexapod - measurement 4

We plot the PSD of the displacement.

  <<plt-matlab>>

PSD_INT of the Z velocity of the Hexapod - measurement 4

And we compute the Cumulative amplitude spectrum.

  <<plt-matlab>>

PSD of the Ry rotation in the Y direction - measurement 4

  <<plt-matlab>>

PSD of the Ty velocity in the Y direction - measurement 4

Turning ON the splipring seems to not add motions on the stages measured. It even seems to lower the motion of the Ty stage (figure fig:meas4_ty_y_psd): does that make any sense?

Turning ON the spindle induces motions:

• at 5Hz on each motion measured
• at 22.5Hz on the Z motion of the Hexapod. Can this is due to some 50Hz?
• at 62Hz on each motion measured

Measurement 5 - Transmission from ground to marble

This measurement just consists of measurement of Y-Z motion of the ground and the marble.

The time domain signals are shown on figure fig:meas5.

  <<plt-matlab>>

Time domain - measurement 5

We compute the PSD of each track and we plot the PSD of the Z motion for the ground and marble on figure fig:meas5_z_psd and for the Y motion on figure fig:meas5_y_psd.

  [pxx51, f51] = pwelch(meas{5}.Track1(:), psd_window, [], [], Fs);
  [pxx52, f52] = pwelch(meas{5}.Track2(:), psd_window, [], [], Fs);
  [pxx53, f53] = pwelch(meas{5}.Track3(:), psd_window, [], [], Fs);
  [pxx54, f54] = pwelch(meas{5}.Track4(:), psd_window, [], [], Fs);

  <<plt-matlab>>

PSD of the ground and marble in the Z direction

  <<plt-matlab>>

PSD of the ground and marble in the Y direction

Then, instead of looking at the Power Spectral Density, we can try to estimate the transfer function from a ground motion to the motion of the marble. The transfer functions are shown on figure fig:meas5_tf and the coherence on figure fig:meas5_coh.

  [tfz, fz] = tfestimate(meas{5}.Track1(:), meas{5}.Track2(:), psd_window, [], [], Fs);
  [tfy, fy] = tfestimate(meas{5}.Track3(:), meas{5}.Track4(:), psd_window, [], [], Fs);

  <<plt-matlab>>

Transfer function estimation - measurement 5

  [cohz, fz] = mscohere(meas{5}.Track1(:), meas{5}.Track2(:), psd_window, [], [], Fs);
  [cohy, fy] = mscohere(meas{5}.Track3(:), meas{5}.Track4(:), psd_window, [], [], Fs);

  <<plt-matlab>>

Coherence - measurement 5

The marble seems to have a resonance at around 20Hz on the Y direction. But the coherence is not good above 20Hz, so it is difficult to estimate resonances.