MFPT Dataset DemonstrationĀ¶
Cloning the damavand repositoryĀ¶
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!git clone https://github.com/pydamavand/damavand
!git clone https://github.com/pydamavand/damavand
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!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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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.
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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, ZipDatasetDownloader
from damavand.damavand.datasets.digestors import MFPT
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, ZipDatasetDownloader
from damavand.damavand.datasets.digestors import MFPT
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()
downloader = ZipDatasetDownloader(addresses['MFPT'])
downloader.download_extract('MFPT.zip', 'MFPT/')
addresses = read_addresses()
downloader = ZipDatasetDownloader(addresses['MFPT'])
downloader.download_extract('MFPT.zip', 'MFPT/')
Instantiating a digestor objectĀ¶
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mfpt = MFPT('MFPT/MFPT Fault Data Sets/', [
'1 - Three Baseline Conditions',
'2 - Three Outer Race Fault Conditions',
'3 - Seven More Outer Race Fault Conditions',
'4 - Seven Inner Race Fault Conditions',
])
mining_params = {
97656: {'win_len': 16671, 'hop_len': 2000},
48828: {'win_len': 8337, 'hop_len': 1000},
}
mfpt.mine(mining_params)
mfpt = MFPT('MFPT/MFPT Fault Data Sets/', [
'1 - Three Baseline Conditions',
'2 - Three Outer Race Fault Conditions',
'3 - Seven More Outer Race Fault Conditions',
'4 - Seven Inner Race Fault Conditions',
])
mining_params = {
97656: {'win_len': 16671, 'hop_len': 2000},
48828: {'win_len': 8337, 'hop_len': 1000},
}
mfpt.mine(mining_params)
Aggregating the data recorded at 48828 HzĀ¶
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df = pd.concat(mfpt.data[48828]).reset_index(drop = True)
df
df = pd.concat(mfpt.data[48828]).reset_index(drop = True)
df
Out[4]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 8331 | 8332 | 8333 | 8334 | 8335 | 8336 | Fs | load | rot_speed | state | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.190937 | 0.378588 | -0.490422 | -1.442238 | -0.051721 | -0.428069 | -0.656600 | 0.154819 | -0.165624 | 0.138256 | ... | 0.477637 | 0.310549 | -0.486780 | 1.008681 | 1.973270 | 0.014804 | 48828 | 5 | 25 | OR |
| 1 | 0.985570 | 1.528132 | 1.166798 | 1.804840 | 0.136606 | -0.444940 | 0.634646 | 0.592880 | -0.172613 | -0.709712 | ... | -0.555015 | -0.437741 | -0.217456 | 0.394339 | 1.813208 | -0.340981 | 48828 | 5 | 25 | OR |
| 2 | -0.710302 | -0.726792 | 0.444288 | -0.230426 | 0.743183 | 2.364421 | -0.180058 | -0.472887 | -0.975517 | -0.169131 | ... | -0.284190 | 0.323076 | -0.155004 | 0.493425 | 0.662359 | -0.422113 | 48828 | 5 | 25 | OR |
| 3 | -0.489143 | 0.019271 | -0.348365 | 0.377997 | 1.174083 | 0.532484 | -0.967210 | -0.215117 | -0.132693 | 0.017302 | ... | -2.915880 | -1.967240 | -1.461447 | -1.681819 | -3.130149 | -1.986240 | 48828 | 5 | 25 | OR |
| 4 | 0.938070 | 0.278812 | 1.789827 | 1.254427 | 2.114565 | 0.990776 | 0.126996 | 0.384187 | 0.611524 | 1.394369 | ... | -0.194616 | -0.122824 | 0.054416 | 0.301516 | -0.707878 | -1.143872 | 48828 | 5 | 25 | OR |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1941 | -0.230820 | -0.790745 | 0.025854 | 0.201064 | -0.548063 | -0.909767 | -0.839439 | -0.679033 | -0.491099 | -0.703546 | ... | 0.509361 | 0.721611 | 0.855363 | 0.290441 | -0.004898 | 0.272807 | 48828 | 3 | 25 | IR |
| 1942 | -0.284608 | -0.134428 | -0.334139 | 1.513390 | 0.178323 | -0.398191 | -1.127591 | -0.356363 | 0.749201 | 0.402916 | ... | -0.213912 | -0.462882 | 0.138403 | 0.439809 | -0.295757 | 0.332970 | 48828 | 3 | 25 | IR |
| 1943 | 0.931031 | 1.508049 | 1.777522 | 0.967493 | 0.836941 | 0.520904 | -0.444128 | -0.654865 | -0.066033 | -0.035638 | ... | -1.742745 | -0.446306 | -0.275613 | -1.514215 | -1.291886 | -0.547804 | 48828 | 3 | 25 | IR |
| 1944 | -0.799814 | -0.954929 | -0.645598 | 1.089000 | 0.379178 | -2.049443 | -1.156781 | -1.620229 | -0.118172 | -0.314733 | ... | -1.741047 | 0.355268 | -0.063067 | 1.540943 | -0.970865 | -3.165983 | 48828 | 3 | 25 | IR |
| 1945 | 1.035470 | 0.888933 | 1.426499 | 1.277414 | 2.548528 | 0.588647 | 0.596621 | 0.003606 | 0.368644 | 0.038259 | ... | -1.019165 | 0.174644 | -0.980685 | -0.502556 | 0.272979 | 0.495246 | 48828 | 3 | 25 | IR |
1946 rows Ć 8341 columns
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signals, metadata = df.iloc[:, : - 4], df.iloc[:, - 4 :]
signals
signals, metadata = df.iloc[:, : - 4], df.iloc[:, - 4 :]
signals
Out[5]:
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ... | 8327 | 8328 | 8329 | 8330 | 8331 | 8332 | 8333 | 8334 | 8335 | 8336 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.190937 | 0.378588 | -0.490422 | -1.442238 | -0.051721 | -0.428069 | -0.656600 | 0.154819 | -0.165624 | 0.138256 | ... | -1.544105 | -0.186346 | 0.480972 | 0.054982 | 0.477637 | 0.310549 | -0.486780 | 1.008681 | 1.973270 | 0.014804 |
| 1 | 0.985570 | 1.528132 | 1.166798 | 1.804840 | 0.136606 | -0.444940 | 0.634646 | 0.592880 | -0.172613 | -0.709712 | ... | -1.792633 | 0.365347 | 0.498470 | -0.398966 | -0.555015 | -0.437741 | -0.217456 | 0.394339 | 1.813208 | -0.340981 |
| 2 | -0.710302 | -0.726792 | 0.444288 | -0.230426 | 0.743183 | 2.364421 | -0.180058 | -0.472887 | -0.975517 | -0.169131 | ... | 0.010177 | -1.666413 | -0.462193 | -0.095431 | -0.284190 | 0.323076 | -0.155004 | 0.493425 | 0.662359 | -0.422113 |
| 3 | -0.489143 | 0.019271 | -0.348365 | 0.377997 | 1.174083 | 0.532484 | -0.967210 | -0.215117 | -0.132693 | 0.017302 | ... | -1.868412 | -0.944457 | -1.729467 | -2.656622 | -2.915880 | -1.967240 | -1.461447 | -1.681819 | -3.130149 | -1.986240 |
| 4 | 0.938070 | 0.278812 | 1.789827 | 1.254427 | 2.114565 | 0.990776 | 0.126996 | 0.384187 | 0.611524 | 1.394369 | ... | -0.996436 | -0.205396 | -0.512019 | -0.346519 | -0.194616 | -0.122824 | 0.054416 | 0.301516 | -0.707878 | -1.143872 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1941 | -0.230820 | -0.790745 | 0.025854 | 0.201064 | -0.548063 | -0.909767 | -0.839439 | -0.679033 | -0.491099 | -0.703546 | ... | 0.085144 | 0.338052 | 0.070881 | 0.367426 | 0.509361 | 0.721611 | 0.855363 | 0.290441 | -0.004898 | 0.272807 |
| 1942 | -0.284608 | -0.134428 | -0.334139 | 1.513390 | 0.178323 | -0.398191 | -1.127591 | -0.356363 | 0.749201 | 0.402916 | ... | -0.506888 | -0.956246 | -0.803839 | -0.162990 | -0.213912 | -0.462882 | 0.138403 | 0.439809 | -0.295757 | 0.332970 |
| 1943 | 0.931031 | 1.508049 | 1.777522 | 0.967493 | 0.836941 | 0.520904 | -0.444128 | -0.654865 | -0.066033 | -0.035638 | ... | -0.495529 | 0.493770 | 0.659627 | -0.834037 | -1.742745 | -0.446306 | -0.275613 | -1.514215 | -1.291886 | -0.547804 |
| 1944 | -0.799814 | -0.954929 | -0.645598 | 1.089000 | 0.379178 | -2.049443 | -1.156781 | -1.620229 | -0.118172 | -0.314733 | ... | -0.995907 | -2.718988 | 1.922668 | 0.230709 | -1.741047 | 0.355268 | -0.063067 | 1.540943 | -0.970865 | -3.165983 |
| 1945 | 1.035470 | 0.888933 | 1.426499 | 1.277414 | 2.548528 | 0.588647 | 0.596621 | 0.003606 | 0.368644 | 0.038259 | ... | 0.596695 | -0.867976 | 0.016121 | 0.215585 | -1.019165 | 0.174644 | -0.980685 | -0.502556 | 0.272979 | 0.495246 |
1946 rows Ć 8337 columns
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metadata
metadata
Out[6]:
| Fs | load | rot_speed | state | |
|---|---|---|---|---|
| 0 | 48828 | 5 | 25 | OR |
| 1 | 48828 | 5 | 25 | OR |
| 2 | 48828 | 5 | 25 | OR |
| 3 | 48828 | 5 | 25 | OR |
| 4 | 48828 | 5 | 25 | OR |
| ... | ... | ... | ... | ... |
| 1941 | 48828 | 3 | 25 | IR |
| 1942 | 48828 | 3 | 25 | IR |
| 1943 | 48828 | 3 | 25 | IR |
| 1944 | 48828 | 3 | 25 | IR |
| 1945 | 48828 | 3 | 25 | IR |
1946 rows Ć 4 columns
Signal ProcessingĀ¶
Envelope ExtractionĀ¶
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signals_env = env(signals)
signals_env.shape
signals_env = env(signals)
signals_env.shape
Out[9]:
(1946, 8337)
FFTĀ¶
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window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 23500], 'bandpass', fs = float(metadata.iloc[0, 0]), output='sos')
window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 23500], 'bandpass', fs = float(metadata.iloc[0, 0]), 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[11]:
(1946, 4168)
ZoomedFFTĀ¶
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signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 2500, 2500, float(metadata.iloc[0, 0]), freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape
signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 2500, 2500, float(metadata.iloc[0, 0]), freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape
Out[12]:
(1946, 2500)
STFTĀ¶
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STFT_window = scipy.signal.windows.hann(2400)
STFT_freq_filter = scipy.signal.butter(25, [5, 23500], 'bandpass', fs = float(metadata.iloc[0, 0]), output='sos')
signals_env_STFT = stft(signals_env, 2400, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape
STFT_window = scipy.signal.windows.hann(2400)
STFT_freq_filter = scipy.signal.butter(25, [5, 23500], 'bandpass', fs = float(metadata.iloc[0, 0]), output='sos')
signals_env_STFT = stft(signals_env, 2400, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape
Out[13]:
(1946, 30, 1200)
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[16]:
| mean | var | rms | |
|---|---|---|---|
| 0 | -0.191650 | 0.914853 | 0.975491 |
| 1 | -0.187638 | 0.934313 | 0.984643 |
| 2 | -0.186269 | 0.949628 | 0.992131 |
| 3 | -0.188875 | 0.920510 | 0.977847 |
| 4 | -0.187438 | 0.954540 | 0.994823 |
| ... | ... | ... | ... |
| 1941 | -0.186669 | 3.676619 | 1.926516 |
| 1942 | -0.184197 | 3.627021 | 1.913361 |
| 1943 | -0.183948 | 3.623858 | 1.912510 |
| 1944 | -0.189699 | 3.634298 | 1.915799 |
| 1945 | -0.188605 | 3.639548 | 1.917060 |
1946 rows Ć 3 columns
VisualizationĀ¶
InĀ [17]:
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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, 8337)
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, 8337)
sns.lineplot(ax=axes[2], x = fft_freq_axis(8337, 48828), 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, 24424)
sns.lineplot(ax=axes[3], x = zoomed_fft_freq_axis(0, 2500, 2500), 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, 2500)
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, 8337)
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, 8337)
sns.lineplot(ax=axes[2], x = fft_freq_axis(8337, 48828), 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, 24424)
sns.lineplot(ax=axes[3], x = zoomed_fft_freq_axis(0, 2500, 2500), 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, 2500)
plt.subplots_adjust(hspace = 0.75)
fig.show()
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t = np.linspace(0, 0.1707, 30)
f = fft_freq_axis(2400, 48828)
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()[::30])
ax.set_yticks(ax.get_yticks()[::2])
fig.show()
t = np.linspace(0, 0.1707, 30)
f = fft_freq_axis(2400, 48828)
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()[::30])
ax.set_yticks(ax.get_yticks()[::2])
fig.show()
InĀ [18]:
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