MEUT Dataset Demonstration¶

Cloning the damavand repository¶

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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
!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
  Attempting uninstall: pytz
    Found existing installation: pytz 2025.2
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      Successfully uninstalled urllib3-2.5.0
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  Attempting uninstall: six
    Found existing installation: six 1.17.0
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  Attempting uninstall: numpy
    Found existing installation: numpy 2.0.2
    Uninstalling numpy-2.0.2:
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  Attempting uninstall: idna
    Found existing installation: idna 3.10
    Uninstalling idna-3.10:
      Successfully uninstalled idna-3.10
  Attempting uninstall: charset-normalizer
    Found existing installation: charset-normalizer 3.4.2
    Uninstalling charset-normalizer-3.4.2:
      Successfully uninstalled charset-normalizer-3.4.2
  Attempting uninstall: certifi
    Found existing installation: certifi 2025.7.14
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      Successfully uninstalled certifi-2025.7.14
  Attempting uninstall: scipy
    Found existing installation: scipy 1.16.0
    Uninstalling scipy-1.16.0:
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  Attempting uninstall: pandas
    Found existing installation: pandas 2.2.2
    Uninstalling pandas-2.2.2:
      Successfully uninstalled pandas-2.2.2
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

Importings¶

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from damavand.damavand.datasets.downloaders import read_addresses, ZipDatasetDownloader
from damavand.damavand.datasets.digestors import MUET
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 MUET
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 object¶

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addresses = read_addresses()
downloader = ZipDatasetDownloader(addresses['MEUT'])
downloader.download_extract('MEUT.zip', 'MEUT/')
addresses = read_addresses()
downloader = ZipDatasetDownloader(addresses['MEUT'])
downloader.download_extract('MEUT.zip', 'MEUT/')

Instantiating a digestor object¶

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dataset = MUET('MEUT/fm6xzxnf36-2/', os.listdir('MEUT/fm6xzxnf36-2/'), [3])
mining_params = {
    'win_len': 10000,
    'hop_len': 5000
}
dataset.mine(mining_params)
dataset = MUET('MEUT/fm6xzxnf36-2/', os.listdir('MEUT/fm6xzxnf36-2/'), [3])
mining_params = {
    'win_len': 10000,
    'hop_len': 5000
}
dataset.mine(mining_params)

Aggregating data over the third channel¶

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df = pd.concat(dataset.data[3]).reset_index(drop = True)
df
df = pd.concat(dataset.data[3]).reset_index(drop = True)
df

Out[4]:

	0	1	2	3	4	5	6	7	8	9	...	9993	9994	9995	9996	9997	9998	9999	state	severity	load
0	1.2666	1.0863	1.2619	1.0262	0.5688	0.8018	1.0815	0.5893	1.3947	1.2990	...	0.8280	0.9742	0.9013	1.2043	1.1234	1.1803	1.4774	inner	1.7mm	300watt
1	0.7622	0.7220	1.1902	0.9127	0.9632	1.2018	0.8078	1.0471	0.8268	0.8127	...	1.0598	0.9710	0.8199	1.4032	0.5488	1.3034	1.1842	inner	1.7mm	300watt
2	1.1204	0.8002	0.6241	0.7893	1.0753	0.8071	0.9605	0.8527	0.7562	1.3141	...	0.6383	1.1305	1.2806	1.0464	0.9988	0.8893	0.7472	inner	1.7mm	300watt
3	0.5021	0.9887	1.1369	0.9777	1.1457	1.2852	0.9475	1.0292	1.2071	1.2955	...	1.0351	1.1757	0.8629	0.8174	1.1683	0.9307	0.9282	inner	1.7mm	300watt
4	0.4603	1.1289	0.7353	1.1372	0.7812	1.1996	0.9217	0.4311	1.2474	0.7464	...	1.1843	1.1004	0.5805	1.1865	0.5676	0.9766	0.9430	inner	1.7mm	300watt
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
948	1.1333	1.0647	1.0057	0.9308	0.9584	0.7640	0.7204	0.8193	0.8335	0.9000	...	1.1683	1.1828	1.1728	1.1462	1.1098	1.0133	0.9573	healthy	-	with with
949	1.1124	1.0830	0.9493	0.9067	0.9229	0.7486	0.7436	0.7156	0.7114	0.7514	...	1.1864	1.1791	1.1298	0.9603	0.9554	0.8857	0.8053	healthy	-	with with
950	0.9498	0.8349	0.8612	0.9097	0.9825	1.0573	1.1616	1.1726	1.1897	1.1852	...	1.1295	1.1739	1.1008	0.9265	0.8579	0.7646	0.7966	healthy	-	with with
951	0.8021	0.6988	0.7541	0.8113	0.8752	0.9022	1.1178	1.1399	1.1692	1.1779	...	1.1629	1.1838	1.1569	1.0788	0.9513	0.9549	0.8471	healthy	-	with with
952	0.7843	0.7991	0.7984	0.8943	0.9371	1.0001	1.1332	1.1315	1.2031	1.1875	...	0.9645	1.0088	1.0618	1.1734	1.1739	1.1378	1.0438	healthy	-	with with

953 rows × 10003 columns

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signals, metadata = df.iloc[:, : -3], df.iloc[:, -3 :]
signals
signals, metadata = df.iloc[:, : -3], df.iloc[:, -3 :]
signals

Out[5]:

	0	1	2	3	4	5	6	7	8	9	...	9990	9991	9992	9993	9994	9995	9996	9997	9998	9999
0	1.2666	1.0863	1.2619	1.0262	0.5688	0.8018	1.0815	0.5893	1.3947	1.2990	...	1.1624	1.1464	0.8923	0.8280	0.9742	0.9013	1.2043	1.1234	1.1803	1.4774
1	0.7622	0.7220	1.1902	0.9127	0.9632	1.2018	0.8078	1.0471	0.8268	0.8127	...	1.1883	0.7536	0.5153	1.0598	0.9710	0.8199	1.4032	0.5488	1.3034	1.1842
2	1.1204	0.8002	0.6241	0.7893	1.0753	0.8071	0.9605	0.8527	0.7562	1.3141	...	0.6905	0.7385	0.7172	0.6383	1.1305	1.2806	1.0464	0.9988	0.8893	0.7472
3	0.5021	0.9887	1.1369	0.9777	1.1457	1.2852	0.9475	1.0292	1.2071	1.2955	...	0.9602	1.1475	1.1205	1.0351	1.1757	0.8629	0.8174	1.1683	0.9307	0.9282
4	0.4603	1.1289	0.7353	1.1372	0.7812	1.1996	0.9217	0.4311	1.2474	0.7464	...	1.3979	0.8986	0.6804	1.1843	1.1004	0.5805	1.1865	0.5676	0.9766	0.9430
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
948	1.1333	1.0647	1.0057	0.9308	0.9584	0.7640	0.7204	0.8193	0.8335	0.9000	...	0.9696	1.1463	1.1494	1.1683	1.1828	1.1728	1.1462	1.1098	1.0133	0.9573
949	1.1124	1.0830	0.9493	0.9067	0.9229	0.7486	0.7436	0.7156	0.7114	0.7514	...	1.1253	1.1974	1.1655	1.1864	1.1791	1.1298	0.9603	0.9554	0.8857	0.8053
950	0.9498	0.8349	0.8612	0.9097	0.9825	1.0573	1.1616	1.1726	1.1897	1.1852	...	1.1352	1.1830	1.1776	1.1295	1.1739	1.1008	0.9265	0.8579	0.7646	0.7966
951	0.8021	0.6988	0.7541	0.8113	0.8752	0.9022	1.1178	1.1399	1.1692	1.1779	...	1.0087	1.1645	1.1937	1.1629	1.1838	1.1569	1.0788	0.9513	0.9549	0.8471
952	0.7843	0.7991	0.7984	0.8943	0.9371	1.0001	1.1332	1.1315	1.2031	1.1875	...	0.9143	0.8392	0.8605	0.9645	1.0088	1.0618	1.1734	1.1739	1.1378	1.0438

953 rows × 10000 columns

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metadata
metadata

Out[6]:

	state	severity	load
0	inner	1.7mm	300watt
1	inner	1.7mm	300watt
2	inner	1.7mm	300watt
3	inner	1.7mm	300watt
4	inner	1.7mm	300watt
...	...	...	...
948	healthy	-	with with
949	healthy	-	with with
950	healthy	-	with with
951	healthy	-	with with
952	healthy	-	with with

953 rows × 3 columns

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metadata['comb'] = metadata['state'] + '_' + metadata['severity'] + '_' + metadata['load']
metadata['comb'].value_counts()
metadata['comb'] = metadata['state'] + '_' + metadata['severity'] + '_' + metadata['load']
metadata['comb'].value_counts()

Out[7]:

	count
comb
outer_1.1mm_200watt	29
inner_0.9mm_100watt	29
inner_1.1mm_300watt	28
inner_1.5mm_200watt	28
inner_0.7mm_100watt	27
outer_0.9mm_100watt	27
outer_0.9mm_200watt	27
inner_1.3mm_100watt	27
outer_1.1mm_300watt	26
outer_1.1mm_100watt	26
inner_1.1mm_200watt	26
inner_1.1mm_100watt	26
inner_1.7mm_200watt	26
inner_0.9mm_200watt	26
outer_1.3mm_200watt	26
inner_1.7mm_100watt	26
outer_1.7mm_100watt	26
outer_1.7mm_300watt	26
outer_1.5mm_300watt	26
inner_1.3mm_200watt	25
outer_1.3mm_100watt	25
inner_0.9mm_300watt	25
outer_0.7mm_100watt	25
inner_1.5mm_100watt	25
outer_1.5mm_100watt	25
outer_0.7mm_200watt	25
inner_1.5mm_300watt	25
outer_1.7mm_200watt	25
outer_1.5mm_200watt	24
inner_1.7mm_300watt	24
inner_1.3mm_300watt	24
outer_1.3mm_300watt	24
inner_0.7mm_200watt	24
outer_0.9mm_300watt	23
healthy_-_without without	22
healthy_-_with with	22
inner_0.7mm_300watt	21
outer_0.7mm_300watt	12

dtype: int64

Signal Processing¶

Envelope Extraction¶

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signals_env = env(signals)
signals_env.shape
signals_env = env(signals)
signals_env.shape

Out[8]:

(953, 10000)

FFT¶

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# Defenition of a window (to avoid leakage error) and a bandpass frequency filter (to both avoid aliasing and DC-component removal)
window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 4500], 'bandpass', fs = 10000, output='sos')
# Defenition of a window (to avoid leakage error) and a bandpass frequency filter (to both avoid aliasing and DC-component removal)
window = scipy.signal.windows.hann(signals_env.shape[1])
freq_filter = scipy.signal.butter(25, [5, 4500], 'bandpass', fs = 10000, 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[10]:

(953, 5000)

ZoomedFFT¶

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signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 1000, 2000, 10000, freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape
signals_env_ZoomedFFT = zoomed_fft(signals_env, 0, 1000, 2000, 10000, freq_filter = freq_filter, window = window)
signals_env_ZoomedFFT.shape

Out[11]:

(953, 2000)

STFT¶

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# Defenition of a window (to avoid leakage error) and a bandpass frequency filter (to both avoid aliasing and DC-component removal)
STFT_window = scipy.signal.windows.hann(1024) # the length of the window must match the `window_len` argument from the stft function
STFT_freq_filter = scipy.signal.butter(25, [5, 4500], 'bandpass', fs = 10000, output='sos')
signals_env_STFT = stft(signals_env, 1024, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape
# Defenition of a window (to avoid leakage error) and a bandpass frequency filter (to both avoid aliasing and DC-component removal)
STFT_window = scipy.signal.windows.hann(1024) # the length of the window must match the `window_len` argument from the stft function
STFT_freq_filter = scipy.signal.butter(25, [5, 4500], 'bandpass', fs = 10000, output='sos')
signals_env_STFT = stft(signals_env, 1024, 200, STFT_freq_filter, STFT_window)
signals_env_STFT.shape

Out[12]:

(953, 45, 512)

Statistical Features¶

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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[14]:

	mean	var	rms
0	0.960899	0.058191	0.990716
1	0.962156	0.059195	0.992441
2	0.964804	0.061620	0.996227
3	0.965389	0.059221	0.995589
4	0.964893	0.054064	0.992514
...	...	...	...
948	0.982739	0.027171	0.996467
949	0.982324	0.027039	0.995992
950	0.980701	0.027028	0.994386
951	0.981535	0.027043	0.995215
952	0.981407	0.026704	0.994919

953 rows × 3 columns

Visualization¶

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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, 10000)

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, 10000)


sns.lineplot(ax=axes[2], x = fft_freq_axis(10000, 10000), 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, 5000)


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, 10000)

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, 10000)


sns.lineplot(ax=axes[2], x = fft_freq_axis(10000, 10000), 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, 5000)


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()

No description has been provided for this image

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t = np.linspace(0, 1, 45)
f = fft_freq_axis(1024, 10000)

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, 1, 45)
f = fft_freq_axis(1024, 10000)

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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