Feature_Extraction.rar_extraction_numpy 均方根_python 波形因子_时域指标_波形特

import csv,codecs import pandas as pd import pywt import os from time import sleep import numpy as np from tqdm import tqdm """用来确定y坐标名称""" def sgn(x): if x>0: return 1 elif x<0: return -1 else: return 0 """小波去噪（wavelets denosing）""" def Wavelts_Denoising(signal, wavefunc, level, startlever,endlever): coeff = pywt.wavedec(signal, wavefunc, mode='sym', level=level) #分解 for i in range(startlever, endlever + 1): cD=coeff[i-1] Sum = 0.0 Sum = Sum + abs(i) N=len(cD) # 选取小波系数层数为 m~n层，尺度系数不需要处理 for j in range(len(cD)): Sum = (1.0 / float(N)) * Sum sigma = (1.0 / 0.6745) * Sum Tr = sigma *np.sqrt(2 * np.log(len(cD))) # 计算阈值 if abs(cD[j])>= Tr: cD[j] = sgn(cD[j])*(abs(cD[j])- Tr) # 向零收缩 else: cD[j] = 0 # 去噪过程 signal_denoised = pywt.waverec(coeff, wavefunc) # 重构 return signal_denoised """峰值""" def Peak_Value(signal,devision_length): m=int(len(signal)/devision_length) aa=[] for i in range(devision_length): a=max(signal[i*m:(i+1)*m]) aa.append(a) return np.mean(aa) """均值""" def Mean_Value(signal): signal=[abs(a) for a in signal] return np.mean(signal) """均方根""" def Root_Mean_Square(signal): length=len(signal) x=sum([a*a for a in signal])/length return np.sqrt(x) """峭度指标""" def Kurtosis_Value(signal): rms1=Root_Mean_Square(signal) length=len(signal) x1=sum([pow(a,4) for a in signal])/length return x1/pow(rms1,4) """定义保存数据函数""" def data_write_csv(file_name, datas): file_csv = codecs.open(file_name,'w+','utf-8') writer = csv.writer(file_csv, delimiter=' ', quotechar=' ', quoting=csv.QUOTE_MINIMAL) for data in datas: writer.writerow(data) print("处理结束,保存文件成功!") """读取所有文件+特征提取""" def signal_processing(signal): signal.columns = ['a'] signal_denoised = Wavelts_Denoising(list(signal['a']), 'haar', 7, 1, 4) pv = Peak_Value(signal_denoised, 10) mv = Mean_Value(signal_denoised) rms = Root_Mean_Square(signal_denoised) par = pv / rms kv = Kurtosis_Value(signal_denoised) ff = rms / mv return [pv, ',', mv, ',', rms, ',', par, ',', kv, ',', ff] """训练集特征+测试集特征""" def run_feature_extraction(files): feature_data=[] print("检测到%2d个数据文件,处理进度："%len(files)) for i in tqdm(range(0,len(files))): sleep(0.05) data=pd.read_csv(files[i],header=None,sep='\t') all_signal=data.iloc[:,0:1] a1=signal_processing(all_signal) feature_data.append(a1) data_write_csv('Feature_Extraction.csv', feature_data) """执行保存数据函数，将处理好的数据保存到Feature Extraction.csv文件""" try: file_location=input(str('文件地址输入格式：C:\\\\testdata\\\\1nd_test\\\\')+"\n请输入数据绝对地址：") for root, dirs, files in os.walk(file_location): files=[file_location+x for x in files] run_feature_extraction(files) except: print("文件地址错误或数据读取错误！")