前缀 ：将一维机械振动信号构造为训练集和测试集（Python）

https://mp.weixin.qq.com/s/DTKjBo6_WAQ7bUPZEdB1TA

import pandas as pd
import numpy as np
import scipy.io as sio
import statistics_hamming
from statistics_hamming import *

signals_train_mat = sio.loadmat('./ProcessedData/signals_train.mat')
signals_train_mat

{'__header__': b'MATLAB 5.0 MAT-file Platform: nt, Created on: Wed May  5 09:16:40 2021','__version__': '1.0','__globals__': [],'Signal': array([[ 0.00470634,  0.00733712,  0.00963905, ...,  0.00536404,0.00470634,  0.00503519],[-0.00614561, -0.00581676, -0.00450137, ...,  0.00503519,0.00799482,  0.0093102 ],[-0.01699756, -0.01436679, -0.01140716, ...,  0.0777104 ,0.09053544,  0.08330081],...,[-0.00219944, -0.0018706 , -0.00154175, ...,  0.00963905,0.0093102 ,  0.00898136],[ 0.00503519,  0.00207557,  0.00174672, ..., -0.00022636,-0.0012129 , -0.00285714],[-0.00252829, -0.00219944, -0.0018706 , ..., -0.01173601,-0.00647446,  0.00043133]]),'Tipo': array(['Outer', 'Outer', 'Inner', 'Sano ', 'Inner', 'Sano ', 'Outer','Inner', 'Inner', 'Outer', 'Sano ', 'Outer', 'Sano ', 'Inner','Outer', 'Sano ', 'Inner', 'Outer', 'Sano ', 'Outer', 'Inner','Inner', 'Inner', 'Outer', 'Sano ', 'Sano ', 'Sano '], dtype='<U5')}

X_train = signals_train_mat['Signal']
y_train = signals_train_mat['Tipo']

signals_test_mat = sio.loadmat('./ProcessedData/signals_test.mat')
X_test = signals_test_mat['Signal']
y_test = signals_test_mat['Tipo']

signal = X_train[0][0:20000]
signal

array([ 0.00470634,  0.00733712,  0.00963905, ..., -0.01206486,-0.01009178, -0.00614561])

kurt_train = []
impulse_factor_train = []
RMS_train = [] 
margin_factor_train = []
skewness_train = []
shape_factor_train = []
peak_to_peak_train = []
crest_factor_train = []for i in range(len(X_train)):for j in range(10):signal = X_train[i][200000 * j : 200000 * (j+1)]kurt, impulse_factor, RMS, margin_factor, skewness, shape_factor, peak_to_peak, crest_factor = parameters_hamming(signal)kurt_train.append(kurt)impulse_factor_train.append(impulse_factor)RMS_train.append(RMS)margin_factor_train.append(margin_factor)skewness_train.append(skewness)shape_factor_train.append(shape_factor)peak_to_peak_train.append(peak_to_peak)crest_factor_train.append(crest_factor)

y_train = signals_train_mat['Tipo']
tipo_train = []
for i in range(len(y_train)):for j in range(10):tipo_train.append(y_train[i])y_train = tipo_train
len(y_train)

270

kurt_test = []
impulse_factor_test = []
RMS_test = [] 
margin_factor_test = []
skewness_test = []
shape_factor_test = []
peak_to_peak_test = []
crest_factor_test = []for i in range(len(X_test)):for j in range(10):signal = X_test[i][200000 * j : 200000 * (j+1)]kurt, impulse_factor, RMS, margin_factor, skewness, shape_factor, peak_to_peak, crest_factor = parameters_hamming(signal)kurt_test.append(kurt)impulse_factor_test.append(impulse_factor)RMS_test.append(RMS)margin_factor_test.append(margin_factor)skewness_test.append(skewness)shape_factor_test.append(shape_factor)peak_to_peak_test.append(peak_to_peak)crest_factor_test.append(crest_factor)

y_test = signals_test_mat['Tipo']
tipo_test = []
for i in range(len(y_test)):for j in range(10):tipo_test.append(y_test[i])y_test = tipo_test
len(y_test)

90

df_train = pd.DataFrame({'Tipo': np.core.defchararray.replace(y_train, ' ', ''), 'Kurtosis': kurt_train,'Impulse factor': impulse_factor_train, 'RMS': RMS_train, 'Margin factor': margin_factor_train, 'Skewness': skewness_train,'Shape factor': shape_factor_train, 'Peak to peak': peak_to_peak_train,'Crest factor': crest_factor_train})

df_test = pd.DataFrame({'Tipo': np.core.defchararray.replace(y_test, ' ', ''), 'Kurtosis': kurt_test, 'Impulse factor': impulse_factor_test, 'RMS': RMS_test, 'Margin factor': margin_factor_test, 'Skewness': skewness_test,'Shape factor': shape_factor_test, 'Peak to peak': peak_to_peak_test,'Crest factor': crest_factor_test})

df_test

df_train.to_csv('./ProcessedData/statistics_10_train.csv', index = False, header = True, sep = ',')
df_test.to_csv('./ProcessedData/statistics_10_test.csv', index = False, header = True, sep = ',')

特征提取辅助函数

import scipy.stats 
from scipy.stats import kurtosis, skew
from scipy import signal
import matplotlib.pyplot as plt
import numpy as npdef highlowfilter(k_filter, input_signal):'''Given a signal, it applies the high pass or low pass band filter to it, depending on the input choice.INPUT:- k_filter: kind of filter applied: * 'hp' high pass* 'low': low pass- input_signal: signal to which to apply the filter '''b, a = signal.butter(3, 0.05, k_filter) zi = signal.lfilter_zi(b, a)z, _ = signal.lfilter(b, a, input_signal, zi = zi * input_signal[0])z2, _ = signal.lfilter(b, a, z, zi = zi * z[0])y = signal.filtfilt(b, a, input_signal)return z, z2, ydef parameters_hamming(xsignal):'''Given the signal 'xsignal', it applies the Hamming window function, a low pass filter, and calculates certain statistics and parameters. '''xsignal = xsignal * signal.hamming(len(xsignal))_, _, x = highlowfilter('low', xsignal)N = len(x)n_inf = max(abs(x))kurt = kurtosis(x)impulse_factor = N * n_inf / sum(abs(x))RMS = np.sqrt(sum(x**2))margin_factor = n_inf / RMS**2skewness = skew(x)shape_factor = N * RMS / sum(abs(x))peak_to_peak = max(x) - min(x)crest_factor = n_inf / RMSreturn kurt, impulse_factor, RMS, margin_factor, skewness, shape_factor, peak_to_peak, crest_factor知乎学术咨询：
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擅长领域：现代信号处理，机器学习，深度学习，数字孪生，时间序列分析，设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。