84 lines
4.2 KiB
Python
84 lines
4.2 KiB
Python
import tensorflow as tf
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from datadeal import labeled_and_piece, loadData
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import numpy as np
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# 导入数据，并进行数据处理
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# 导入第一类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA')
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data1 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮表面磨损故障恒速\DATA'))
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dataWithLabel1 = labeled_and_piece.GetLabel(data1, 0.000, False)
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(data1, dataWithLabel1) = dataWithLabel1(data1, 0.000, False)
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# 导入第二类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA')
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data2 = np.array(data0(9, 'E:\DDS_data\平行齿轮箱齿轮齿根裂纹故障恒速\DATA'))
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dataWithLabel2 = labeled_and_piece.GetLabel(data2, 1.000, False)
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(data2, dataWithLabel2) = dataWithLabel2(data2, 1.000, False)
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# 导入第三类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA')
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data3 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮断齿故障恒速\DATA'))
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dataWithLabel3 = labeled_and_piece.GetLabel(data3, 2.000, False)
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(data3, dataWithLabel3) = dataWithLabel3(data3, 2.000, False)
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# 导入第四类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA')
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data4 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮偏心故障恒速\DATA'))
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dataWithLabel4 = labeled_and_piece.GetLabel(data4, 3.000, False)
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(data4, dataWithLabel4) = dataWithLabel4(data4, 3.000, False)
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# 导入第五类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA')
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data5 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱齿轮缺齿故障恒速\DATA'))
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dataWithLabel5 = labeled_and_piece.GetLabel(data5, 4.000, False)
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(data5, dataWithLabel5) = dataWithLabel5(data5, 4.000, False)
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# 导入第六类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA')
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data6 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承复合故障恒速\DATA'))
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dataWithLabel6 = labeled_and_piece.GetLabel(data6, 5.000, False)
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(data6, dataWithLabel6) = dataWithLabel6(data6, 5.000, False)
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# 导入第七类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA')
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data7 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承滚动体故障恒速\DATA'))
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dataWithLabel7 = labeled_and_piece.GetLabel(data7, 6.000, False)
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(data7, dataWithLabel7) = dataWithLabel7(data7, 6.000, False)
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# 导入第八类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA')
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data8 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承内圈故障恒速\DATA'))
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dataWithLabel8 = labeled_and_piece.GetLabel(data8, 7.000, False)
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(data8, dataWithLabel8) = dataWithLabel8(data8, 7.000, False)
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# 导入第九类故障并打标签
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data0 = loadData.DataDeal4(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA')
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data9 = np.array(data0(9, 'E:\data\DDS_data\平行齿轮箱轴承外圈故障恒速\DATA'))
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dataWithLabel9 = labeled_and_piece.GetLabel(data9, 8.000, False)
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(data9, dataWithLabel9) = dataWithLabel9(data9, 8.000, False)
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# 合并
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data_all = tf.concat([data1, data2, data3, data4, data5, data6, data7, data8, data9], axis=0)
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label_all = tf.concat(
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[dataWithLabel1, dataWithLabel2, dataWithLabel3, dataWithLabel4, dataWithLabel5, dataWithLabel6, dataWithLabel7,
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dataWithLabel8, dataWithLabel9], axis=0)
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# print("data_all",data_all)
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# print("label_all：",label_all)
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# data_all = np.array(data_all)
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# 划分训练集和测试集,并打乱
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data_new = labeled_and_piece.PieceAndBag_new(data_all, label_all, False)
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(train_data, train_label), (test_data, test_label) = data_new(data_all, label_all, False)
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np.save("train_data.npy",train_data)
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np.save("train_label.npy",train_label)
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np.save("test_data.npy",test_data)
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np.save("test_label.npy",test_label)
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# confusion_matrix(test_label,y_pred=?) //混淆矩阵的得法
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# TSNE.fit_transform() //T-SNE降维表示
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'''train_data.shape: (7776, 80, 80, 9)
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train_label.shape: (7776, 1)
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test_data.shape: (2592, 80, 80, 9)
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test_label.shape: (2592, 1)''' |