关于CWRU轴承数据集的分类

对CWRU数据集中12K采样频率下的驱动端轴承故障数据，电机近似转速=1797的数据进行四分类。内圈故障、滚动体故障、外圈故障（中心方向@6:00）、正常

file name	label
0.000-Normal.mat	0
0.007-Ball.mat	1
0.007-InnerRace.mat	2
0.007-OuterRace6.mat	3
0.014-Ball.mat	1
0.014-InnerRace.mat	2
0.014-OuterRace6.mat	3

读取数据时，采样率=400，训练数据1824组，测试数据610组
epochs=100、batch size=32
结果在训练集的识别率为1，测试集的识别率为0.9902

测试集结果

	Normal	Ball	Inner Race	Outer Race
Normal	153	0	0	0
Ball	0	153	0	0
Inner Race	153	0	146	0
Outer Race	153	0	6	152

Model

    model = Sequential()
    model.add(Conv1D(16, 16, activation='relu',
                     input_shape=(subsample, 1)))
    model.add(Conv1D(32, 8, activation='relu', padding="same"))
    model.add(MaxPooling1D(8))
    model.add(Conv1D(64, 4, activation='relu', padding="same"))
    model.add(Conv1D(64, 4, activation='relu', padding="same"))
    model.add(MaxPooling1D(4))
    model.add(Conv1D(256, 2, activation='relu', padding="same"))
    model.add(Conv1D(256, 2, activation='relu', padding="same"))
    model.add(MaxPooling1D(2))
    model.add(Conv1D(512, 1, activation='relu', padding="same"))
    model.add(Conv1D(512, 1, activation='relu', padding="same"))
    model.add(MaxPooling1D(1))

    model.add(GlobalAveragePooling1D())

    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.3))
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.3))

    model.add(Dense(labels, activation='softmax'))

    opt = Adam(0.0002)

    model.compile(loss='categorical_crossentropy',
                  optimizer=opt, metrics=['accuracy'])

原文链接：https://blog.csdn.net/qq_27389705/article/details/100990370