84 lines
4.2 KiB
Python
84 lines
4.2 KiB
Python
from matplotlib import numpy
|
|
import matplotlib.pyplot as plot
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
import os
|
|
import loadData
|
|
import labeled_and_piece
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
|
|
dataWithLabel1 = labeled_and_piece.ConcatLabel(data, 1, True)
|
|
dataWithLabel1 = np.array(dataWithLabel1(data, 1, True))
|
|
|
|
# 导入第二类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
|
|
dataWithLabel2 = labeled_and_piece.ConcatLabel(data, 2, True)
|
|
dataWithLabel2 = np.array(dataWithLabel2(data, 2, True))
|
|
|
|
# 导入第三类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
|
|
dataWithLabel3 = labeled_and_piece.ConcatLabel(data, 3, True)
|
|
dataWithLabel3 = np.array(dataWithLabel3(data, 3, True))
|
|
|
|
# 导入第四类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
|
|
dataWithLabel4 = labeled_and_piece.ConcatLabel(data, 4, True)
|
|
dataWithLabel4 = np.array(dataWithLabel4(data, 4, True))
|
|
|
|
# 导入第五类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
|
|
dataWithLabel5 = labeled_and_piece.ConcatLabel(data, 5, True)
|
|
dataWithLabel5 = labeled_and_piece.np.array(dataWithLabel5(data, 5, True))
|
|
|
|
# 导入第六类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
|
|
dataWithLabel6 = labeled_and_piece.ConcatLabel(data, 6, True)
|
|
dataWithLabel6 = np.array(dataWithLabel6(data, 6, True))
|
|
|
|
# 导入第七类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
|
|
dataWithLabel7 = labeled_and_piece.ConcatLabel(data, 7, True)
|
|
dataWithLabel7 = np.array(dataWithLabel7(data, 7, True))
|
|
|
|
# 导入第八类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
|
|
dataWithLabel8 = labeled_and_piece.ConcatLabel(data, 8, True)
|
|
dataWithLabel8 = np.array(dataWithLabel8(data, 8, True))
|
|
|
|
# 导入第九类故障并打标签
|
|
data0 = loadData.DataDeal1(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
|
|
data = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
|
|
dataWithLabel9 = labeled_and_piece.ConcatLabel(data, 9, True)
|
|
dataWithLabel9 = np.array(dataWithLabel9(data, 9, True))
|
|
|
|
data_all = tf.concat(
|
|
[dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
|
|
dataWithLabel8, dataWithLabel9], axis=1)
|
|
|
|
data_all = np.array(data_all)
|
|
print(data_all.shape)
|
|
data_all =tf.transpose(data,[1,2,0])
|
|
print(data_all.shape)
|
|
data_train = tf.random.shuffle(data_all)
|
|
print(data_train)
|
|
data_all = tf.transpose(data, [2, 0, 1])
|
|
data_new = labeled_and_piece.PieceAndBag(data_train, True)
|
|
(train_data, train_label), (test_data, test_label) = data_new(data_train, True)
|
|
train_data = np.array(train_data)
|
|
train_label = np.array(train_label)
|
|
test_data = np.array(test_data)
|
|
test_label = np.array(test_label)
|
|
|
|
print(train_data.shape)
|
|
print(train_label.shape)
|
|
print(test_data.shape)
|
|
print(test_label.shape)
|
|
|