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improved the motor resonance plotting to avoid low freq detection

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This commit is contained in:
Félix Boisselier
2024-04-02 18:01:22 +02:00
parent c2c05e51ae
commit fa41637ac9
3 changed files with 35 additions and 19 deletions
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27
K-ShakeTune/scripts/common_func.py
View File

@@ -72,8 +72,25 @@ def compute_spectrogram(data):
# Compute natural resonant frequency and damping ratio by using the half power bandwidth method with interpolated frequencies
def compute_mechanical_parameters(psd, freqs):
max_power_index = np.argmax(psd)
def compute_mechanical_parameters(psd, freqs, min_freq=None):
max_under_min_freq = False
if min_freq is not None:
min_freq_index = np.searchsorted(freqs, min_freq, side='left')
if min_freq_index >= len(freqs):
return None, None, None, max_under_min_freq
if np.argmax(psd) < min_freq_index:
max_under_min_freq = True
else:
min_freq_index = 0
# Consider only the part of the signal above min_freq
psd_above_min_freq = psd[min_freq_index:]
if len(psd_above_min_freq) == 0:
return None, None, None, max_under_min_freq
max_power_index_above_min_freq = np.argmax(psd_above_min_freq)
max_power_index = max_power_index_above_min_freq + min_freq_index
fr = freqs[max_power_index]
max_power = psd[max_power_index]
@@ -81,9 +98,9 @@ def compute_mechanical_parameters(psd, freqs):
indices_below = np.where(psd[:max_power_index] <= half_power)[0]
indices_above = np.where(psd[max_power_index:] <= half_power)[0]
# If we are not able to find points around the half power, we can't compute the damping ratio and return None instead
if len(indices_below) == 0 or len(indices_above) == 0:
# If we are not able to find points around the half power, we can't compute the damping ratio and return None instead
return fr, None, max_power_index
return fr, None, max_power_index, max_under_min_freq
idx_below = indices_below[-1]
idx_above = indices_above[0] + max_power_index
@@ -96,7 +113,7 @@ def compute_mechanical_parameters(psd, freqs):
zeta = math.sqrt(0.5 - math.sqrt(1 / (4 + 4 * bw1 - bw2)))
return fr, zeta, max_power_index
return fr, zeta, max_power_index, max_under_min_freq
# This find all the peaks in a curve by looking at when the derivative term goes from positive to negative
# Then only the peaks found above a threshold are kept to avoid capturing peaks in the low amplitude noise of a signal

2
K-ShakeTune/scripts/graph_shaper.py
View File

@@ -51,7 +51,7 @@ def calibrate_shaper(datas, max_smoothing, scv, max_freq):
calibration_data = helper.process_accelerometer_data(datas)
calibration_data.normalize_to_frequencies()
fr, zeta, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
fr, zeta, _, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
# If the damping ratio computation fail, we use Klipper default value instead
if zeta is None: zeta = 0.1

25
K-ShakeTune/scripts/graph_vibrations.py
View File

@@ -86,7 +86,7 @@ def compute_motor_profiles(freqs, psds, all_angles_energy, measured_angles=[0, 9
def compute_dir_speed_spectrogram(measured_speeds, data, kinematics="cartesian", measured_angles=[0, 90]):
# We want to project the motor vibrations measured on their own axes on the [0, 360] range
spectrum_angles = np.linspace(0, 360, 720) # One point every 0.5 degrees
spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 5) # 5 points between each speed measurements
spectrum_speeds = np.linspace(min(measured_speeds), max(measured_speeds), len(measured_speeds) * 6)
spectrum_vibrations = np.zeros((len(spectrum_angles), len(spectrum_speeds)))
def get_interpolated_vibrations(data, speed, speeds):
@@ -269,8 +269,8 @@ def plot_speed_profile(ax, all_speeds, sp_min_energy, sp_max_energy, sp_avg_ener
ha='left', fontsize=13, color=KLIPPAIN_COLORS['red_pink'], zorder=10)
for idx, (start, end, _) in enumerate(low_energy_zones):
ax2.axvline(all_speeds[start], y2min, sp_composite_variance[start]/y2max, color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax2.axvline(all_speeds[end], y2min, sp_composite_variance[start]/y2max, color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax2.axvline(all_speeds[start], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax2.axvline(all_speeds[end], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5)
ax2.fill_between(all_speeds[start:end], y2min, sp_composite_variance[start:end], color='green', alpha=0.2, label=f'Zone {idx+1}: {all_speeds[start]:.1f} to {all_speeds[end]:.1f} mm/s')
ax.xaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
@@ -305,19 +305,18 @@ def plot_motor_profiles(ax, freqs, main_angles, motor_profiles, global_motor_pro
ax.set_ylim([0, max_value * 1.1])
# Then add the motor resonance peak to the graph and print some infos about it
motor_fr, motor_zeta, motor_res_idx = compute_mechanical_parameters(global_motor_profile, freqs)
if motor_fr > 25:
if motor_zeta is not None:
print_with_c_locale("Motors have a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (motor_fr, motor_zeta))
else:
print_with_c_locale("Motors have a main resonant frequency at %.1fHz but it was impossible to estimate a damping ratio." % (motor_fr))
motor_fr, motor_zeta, motor_res_idx, lowfreq_max = compute_mechanical_parameters(global_motor_profile, freqs, 30)
if lowfreq_max:
print_with_c_locale("[WARNING] There are a lot of low frequency vibrations that can alter the readings. This is probably due to the test being performed at too high an acceleration!")
print_with_c_locale("Try lowering the ACCEL value and/or increasing the SIZE value before restarting the macro to ensure that only constant speeds are being recorded and that the dynamic behavior of the machine is not affecting the measurements")
if motor_zeta is not None:
print_with_c_locale("Motors have a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (motor_fr, motor_zeta))
else:
print_with_c_locale("The detected resonance frequency of the motors is too low (%.1fHz). This is probably due to the test run with too high acceleration!" % motor_fr)
print_with_c_locale("Try lowering the ACCEL value before restarting the macro to ensure that only constant speeds are recorded and that the dynamic behavior of the machine is not impacting the measurements.")
print_with_c_locale("Motors have a main resonant frequency at %.1fHz but it was impossible to estimate a damping ratio." % (motor_fr))
ax.plot(freqs[motor_res_idx], global_motor_profile[motor_res_idx], "x", color='black', markersize=10)
ax.annotate(f"R", (freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
textcoords="offset points", xytext=(15, 5),
ax.annotate(f"R", (freqs[motor_res_idx], global_motor_profile[motor_res_idx]),
textcoords="offset points", xytext=(15, 5),
ha='right', fontsize=14, color=KLIPPAIN_COLORS['red_pink'], weight='bold')
legend_texts = ["Resonant frequency (ω0): %.1fHz" % (motor_fr)]