PU Dataset DemonstrationĀ¶
Cloning the damavand repositoryĀ¶
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!git clone https://github.com/pydamavand/damavand
!git clone https://github.com/pydamavand/damavand
Cloning into 'damavand'... remote: Enumerating objects: 263, done. remote: Counting objects: 100% (263/263), done. remote: Compressing objects: 100% (197/197), done. remote: Total 263 (delta 132), reused 196 (delta 65), pack-reused 0 (from 0) Receiving objects: 100% (263/263), 6.62 MiB | 12.75 MiB/s, done. Resolving deltas: 100% (132/132), done.
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!pip install -r damavand/requirements.txt
!pip install -r damavand/requirements.txt
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Installing collected packages: pytz, urllib3, tzdata, six, rarfile, numpy, idna, charset-normalizer, certifi, scipy, pandas
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Attempting uninstall: pandas
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.1.4 which is incompatible.
opencv-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= "3.9", but you have numpy 1.26.4 which is incompatible.
opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= "3.9", but you have numpy 1.26.4 which is incompatible.
opencv-contrib-python 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= "3.9", but you have numpy 1.26.4 which is incompatible.
plotnine 0.14.5 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.
tsfresh 0.21.0 requires scipy>=1.14.0; python_version >= "3.10", but you have scipy 1.13.1 which is incompatible.
thinc 8.3.6 requires numpy<3.0.0,>=2.0.0, but you have numpy 1.26.4 which is incompatible.
xarray 2025.7.1 requires pandas>=2.2, but you have pandas 2.1.4 which is incompatible.
mizani 0.13.5 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.
Successfully installed certifi-2024.7.4 charset-normalizer-3.3.2 idna-3.7 numpy-1.26.4 pandas-2.1.4 pytz-2024.1 rarfile-4.2 scipy-1.13.1 six-1.16.0 tzdata-2024.1 urllib3-2.2.2
ImportingsĀ¶
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from damavand.damavand.datasets.downloaders import read_addresses, PuDownloader
from damavand.damavand.datasets.digestors import PU
from damavand.damavand.signal_processing.transformations import *
from damavand.damavand.signal_processing.feature_extraction import *
from damavand.damavand.utils import *
import os
import pandas as pd
import numpy as np
import scipy
from damavand.damavand.datasets.downloaders import read_addresses, PuDownloader
from damavand.damavand.datasets.digestors import PU
from damavand.damavand.signal_processing.transformations import *
from damavand.damavand.signal_processing.feature_extraction import *
from damavand.damavand.utils import *
import os
import pandas as pd
import numpy as np
import scipy
Instantiating a downloader objectĀ¶
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addresses = read_addresses()
addresses['PU'].pop('real_damage')
downloader = PuDownloader(addresses['PU'])
downloader.download_extract(download_path = 'PU_compressed/', extraction_path = 'PU/', timeout = 10)
addresses = read_addresses()
addresses['PU'].pop('real_damage')
downloader = PuDownloader(addresses['PU'])
downloader.download_extract(download_path = 'PU_compressed/', extraction_path = 'PU/', timeout = 10)
Downloading: K001.rar Downloading: K002.rar Downloading: K003.rar Downloading: K004.rar Downloading: K005.rar Downloading: K006.rar Downloading: KA01.rar Downloading: KA03.rar Downloading: KA05.rar Downloading: KA06.rar Downloading: KA07.rar Downloading: KA08.rar Downloading: KA09.rar Downloading: KI01.rar Downloading: KI03.rar Downloading: KI05.rar Downloading: KI07.rar Downloading: KI08.rar Extracting: K001.rar Extracting: K002.rar Extracting: K003.rar Extracting: K004.rar Extracting: K005.rar Extracting: K006.rar Extracting: KA01.rar Extracting: KA03.rar Extracting: KA05.rar Extracting: KA06.rar Extracting: KA07.rar Extracting: KA08.rar Extracting: KA09.rar Extracting: KI01.rar Extracting: KI03.rar Extracting: KI05.rar Extracting: KI07.rar Extracting: KI08.rar
Instantiating a digestor objectĀ¶
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mining_params = {'win_len': 16000, 'hop_len': 16000}
pu = PU('PU/', os.listdir('PU/'), ['Vib'],reps = [1])
pu.mine(mining_params)
mining_params = {'win_len': 16000, 'hop_len': 16000}
pu = PU('PU/', os.listdir('PU/'), ['Vib'],reps = [1])
pu.mine(mining_params)
Aggregating vibration dataĀ¶
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df = pd.concat(pu.data['Vib']).reset_index(drop = True)
df
df = pd.concat(pu.data['Vib']).reset_index(drop = True)
df
Signals-Metadata declarationĀ¶
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signals, metadata = df.iloc[:, : -5], df.iloc[:, -5 :]
signals
signals, metadata = df.iloc[:, : -5], df.iloc[:, -5 :]
signals
Out[5]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 15990 | 15991 | 15992 | 15993 | 15994 | 15995 | 15996 | 15997 | 15998 | 15999 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.012207 | 0.057983 | 0.085449 | 0.177002 | 0.039673 | 0.027466 | 0.030518 | -0.003052 | 0.018311 | -0.051880 | ... | -0.158691 | -0.115967 | 0.009155 | 0.170898 | 0.195312 | 0.189209 | -0.018311 | -0.213623 | -0.146484 | -0.042725 |
| 1 | -0.033569 | -0.067139 | -0.231934 | -0.149536 | -0.177002 | -0.009155 | 0.067139 | 0.088501 | -0.061035 | -0.231934 | ... | -0.082397 | 0.500488 | 0.653076 | 0.244141 | -0.363159 | -0.650024 | -0.491333 | 0.061035 | 0.424194 | 0.360107 |
| 2 | -0.125122 | -0.680542 | -0.949097 | -0.793457 | -0.360107 | -0.045776 | -0.067139 | -0.189209 | -0.286865 | -0.115967 | ... | 0.692749 | 0.692749 | 0.802612 | 0.524902 | -0.070190 | -0.701904 | -1.309204 | -1.556396 | -1.226807 | -1.062012 |
| 3 | -0.851440 | -0.515747 | -0.225830 | 0.033569 | 0.262451 | 0.360107 | 0.140381 | -0.082397 | -0.338745 | -0.280762 | ... | -0.338745 | -0.180054 | -0.305176 | 0.143433 | 0.137329 | 0.787354 | 0.100708 | 0.250244 | -0.082397 | 0.274658 |
| 4 | 0.030518 | -0.744629 | -0.073242 | 0.335693 | -0.140381 | 0.885010 | 0.137329 | 1.092529 | 0.027466 | -0.967407 | ... | -0.186157 | -0.073242 | 0.045776 | 0.183105 | 0.146484 | 0.094604 | 1.336670 | 0.100708 | 0.765991 | 0.332642 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1147 | -0.057983 | 0.100708 | -0.048828 | 0.152588 | 0.125122 | 0.064087 | -0.048828 | 0.012207 | -0.283813 | -0.054932 | ... | 0.109863 | -0.207520 | 0.125122 | 0.161743 | 0.109863 | 0.210571 | -0.012207 | -0.146484 | -0.155640 | -0.234985 |
| 1148 | -0.119019 | 0.067139 | 0.009155 | 0.180054 | 0.051880 | 0.509644 | 0.009155 | 0.106812 | 0.030518 | 0.051880 | ... | -0.146484 | -0.079346 | -0.198364 | -0.137329 | -0.131226 | -0.054932 | 0.003052 | 0.119019 | -0.054932 | 0.076294 |
| 1149 | 0.018311 | 0.015259 | -0.036621 | -0.012207 | -0.027466 | -0.057983 | -0.027466 | -0.079346 | 0.039673 | 0.006104 | ... | -0.085449 | 0.204468 | -0.054932 | -0.057983 | -0.042725 | 1.083374 | -0.134277 | 0.161743 | -0.094604 | -0.436401 |
| 1150 | 0.051880 | 1.135254 | 0.042725 | -0.802612 | -0.082397 | -0.836182 | -0.140381 | -0.262451 | -0.003052 | 0.250244 | ... | 0.000000 | -0.131226 | -0.036621 | -0.085449 | -0.109863 | -0.097656 | -0.186157 | -0.149536 | 1.480103 | -0.057983 |
| 1151 | -0.042725 | 0.039673 | 0.076294 | 0.030518 | 0.024414 | 0.094604 | 0.131226 | 0.085449 | 0.021362 | -0.024414 | ... | 0.000000 | -0.042725 | 0.003052 | -0.143433 | -0.094604 | -0.094604 | -0.024414 | -0.045776 | 0.024414 | -0.018311 |
1152 rows Ć 16000 columns
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metadata
metadata
Out[6]:
| rot_speed | load_torque | radial_force | code | rep | |
|---|---|---|---|---|---|
| 0 | N15 | M07 | F04 | KI01 | 1 |
| 1 | N15 | M07 | F04 | KI01 | 1 |
| 2 | N15 | M07 | F04 | KI01 | 1 |
| 3 | N15 | M07 | F04 | KI01 | 1 |
| 4 | N15 | M07 | F04 | KI01 | 1 |
| ... | ... | ... | ... | ... | ... |
| 1147 | N09 | M07 | F10 | K001 | 1 |
| 1148 | N09 | M07 | F10 | K001 | 1 |
| 1149 | N09 | M07 | F10 | K001 | 1 |
| 1150 | N09 | M07 | F10 | K001 | 1 |
| 1151 | N09 | M07 | F10 | K001 | 1 |
1152 rows Ć 5 columns
Signal ProcessingĀ¶
Envelope ExtractionĀ¶
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signals_env = env(signals)
signals_env.shape
signals_env = env(signals)
signals_env.shape
Out[7]:
(1152, 16000)
FFTĀ¶
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window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 31000], 'bandpass', fs = 64000, output='sos')
window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 31000], 'bandpass', fs = 64000, output='sos')
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signals_env_fft = fft(signals_env, freq_filter = freq_filter, window = window)
signals_env_fft.shape
signals_env_fft = fft(signals_env, freq_filter = freq_filter, window = window)
signals_env_fft.shape
Out[9]:
(1152, 8000)
Zoomed FFTĀ¶
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signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 1000, 2000, 64000, freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape
signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 1000, 2000, 64000, freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape
Out[10]:
(1152, 2000)
STFTĀ¶
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STFT_window = scipy.signal.windows.hann(2000)
STFT_freq_filter = scipy.signal.butter(25, [5, 31000], 'bandpass', fs = 64000, output='sos')
signals_env_STFT = stft(signals_env, 2000, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape
STFT_window = scipy.signal.windows.hann(2000)
STFT_freq_filter = scipy.signal.butter(25, [5, 31000], 'bandpass', fs = 64000, output='sos')
signals_env_STFT = stft(signals_env, 2000, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape
Out[11]:
(1152, 71, 1000)
Statistical FeaturesĀ¶
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# Defining the feature-set to be extracted
features = {'mean': (np.mean, (), {}), 'var': (np.var, (), {}), 'rms': (rms, (), {})}
# Defining the feature-set to be extracted
features = {'mean': (np.mean, (), {}), 'var': (np.var, (), {}), 'rms': (rms, (), {})}
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# Extracting the desired feature-set from time-domain signals
features_df = feature_extractor(signals, features)
features_df
# Extracting the desired feature-set from time-domain signals
features_df = feature_extractor(signals, features)
features_df
Out[17]:
| mean | var | rms | |
|---|---|---|---|
| 0 | -0.014983 | 0.267011 | 0.516949 |
| 1 | -0.014994 | 0.357743 | 0.598304 |
| 2 | -0.015197 | 0.325597 | 0.570813 |
| 3 | -0.011013 | 0.286674 | 0.535533 |
| 4 | -0.011621 | 0.383353 | 0.619264 |
| ... | ... | ... | ... |
| 1147 | -0.013029 | 0.120181 | 0.346917 |
| 1148 | -0.019953 | 0.138645 | 0.372884 |
| 1149 | -0.019041 | 0.140663 | 0.375533 |
| 1150 | -0.014897 | 0.137413 | 0.370991 |
| 1151 | -0.011115 | 0.132731 | 0.364493 |
1152 rows Ć 3 columns
VisualizationĀ¶
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import seaborn as sns
from matplotlib import pyplot as plt
sns.set()
fig, axes = plt.subplots(4, 1, figsize = (16, 10))
sns.lineplot(ax=axes[0], x=range(len(signals.iloc[0,:])), y = signals.iloc[0,:])
axes[0].set_title("Original Time Signal")
axes[0].set_ylabel("Amplitude")
axes[0].set_xlabel("sample")
axes[0].set_xlim(0, 16000)
sns.lineplot(ax=axes[1], x=range(len(signals_env.iloc[0,:])), y = signals_env.iloc[0, :])
axes[1].set_title("Envelope")
axes[1].set_ylabel("Amplitude")
axes[1].set_xlabel("sample")
axes[1].set_xlim(0, 16000)
sns.lineplot(ax=axes[2], x = fft_freq_axis(16000, 64000), y = signals_env_fft.iloc[0, :])
axes[2].set_title("FFT")
axes[2].set_ylabel("Amplitude")
axes[2].set_xlabel("Frequency (Hz)")
axes[2].set_xlim(0, 32000)
sns.lineplot(ax=axes[3], x = zoomed_fft_freq_axis(0, 1000, 2000), y = signals_env_ZoomedFFT.iloc[0, :])
axes[3].set_title("Zoomed FFT")
axes[3].set_ylabel("Amplitude")
axes[3].set_xlabel("Frequency (Hz)")
axes[3].set_xlim(0, 1000)
plt.subplots_adjust(hspace = 0.75)
fig.show()
import seaborn as sns
from matplotlib import pyplot as plt
sns.set()
fig, axes = plt.subplots(4, 1, figsize = (16, 10))
sns.lineplot(ax=axes[0], x=range(len(signals.iloc[0,:])), y = signals.iloc[0,:])
axes[0].set_title("Original Time Signal")
axes[0].set_ylabel("Amplitude")
axes[0].set_xlabel("sample")
axes[0].set_xlim(0, 16000)
sns.lineplot(ax=axes[1], x=range(len(signals_env.iloc[0,:])), y = signals_env.iloc[0, :])
axes[1].set_title("Envelope")
axes[1].set_ylabel("Amplitude")
axes[1].set_xlabel("sample")
axes[1].set_xlim(0, 16000)
sns.lineplot(ax=axes[2], x = fft_freq_axis(16000, 64000), y = signals_env_fft.iloc[0, :])
axes[2].set_title("FFT")
axes[2].set_ylabel("Amplitude")
axes[2].set_xlabel("Frequency (Hz)")
axes[2].set_xlim(0, 32000)
sns.lineplot(ax=axes[3], x = zoomed_fft_freq_axis(0, 1000, 2000), y = signals_env_ZoomedFFT.iloc[0, :])
axes[3].set_title("Zoomed FFT")
axes[3].set_ylabel("Amplitude")
axes[3].set_xlabel("Frequency (Hz)")
axes[3].set_xlim(0, 1000)
plt.subplots_adjust(hspace = 0.75)
fig.show()
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t = np.linspace(0, 0.25, 70)
f = fft_freq_axis(2000, 64000)
fig, ax = plt.subplots(figsize = (16, 8))
ax = sns.heatmap(signals_env_STFT[0, :, :], xticklabels = np.round(f, decimals = 2), yticklabels = np.round(t, decimals = 2), annot = False, cbar = False)
ax.set(xlabel = 'Frequency (Hz)', ylabel = 'Time (sec)')
ax.set_title('STFT')
ax.set_xticks(ax.get_xticks()[::20])
ax.set_yticks(ax.get_yticks()[::5])
fig.show()
t = np.linspace(0, 0.25, 70)
f = fft_freq_axis(2000, 64000)
fig, ax = plt.subplots(figsize = (16, 8))
ax = sns.heatmap(signals_env_STFT[0, :, :], xticklabels = np.round(f, decimals = 2), yticklabels = np.round(t, decimals = 2), annot = False, cbar = False)
ax.set(xlabel = 'Frequency (Hz)', ylabel = 'Time (sec)')
ax.set_title('STFT')
ax.set_xticks(ax.get_xticks()[::20])
ax.set_yticks(ax.get_yticks()[::5])
fig.show()
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