【代码分享】手把手教你：基于深度学习的滚动轴承故障诊断

from tensorflow import keras from tensorflow.keras import layers, models class CNN_ResNet_model: def __init__(self, label_num , num_b , data_shape=(1000, 2)): # 默认输入张量为(1000,2) # res块数量 self.num_blocks = num_b # 过滤器数量 self.filters = 64 # 步长 self.conv_size = 3 # 分类类别数 self.label_num = label_num # 数据输入的shape self.data_shape = data_shape self.loss = 'sparse_categorical_crossentropy' self.metrics = ['acc'] def res_net_block(self, input_data): # CNN层 x = layers.Conv1D(self.filters, self.conv_size, activation='relu', padding='same')(input_data) x = layers.BatchNormalization()(x) x = layers.Conv1D(self.filters, self.conv_size, activation=None, padding='same')(x) # 第二层没有激活函数 x = layers.BatchNormalization()(x) # 两个张量相加 x = layers.Add()([x, input_data]) # 对相加的结果使用ReLU激活 x = layers.Activation('relu')(x) # 返回结果 return x def model_create(self, learning_rate): inputs = keras.Input(shape=(self.data_shape[0], self.data_shape[1])) x = layers.Conv1D(32, 3, activation='relu')(inputs) x = layers.Conv1D(64, 3, activation='relu')(x) x = layers.MaxPooling1D(16)(x) # 定义res层 for i in range(self.num_blocks): x = self.res_net_block(x) # 添加一个CNN层 x = layers.Conv1D(64, 3, activation='relu')(x) # 全局平均池化GAP层 x = layers.GlobalAveragePooling1D()(x) # 几个密集分类层 x = layers.Dense(256, activation='relu')(x) # 退出层 x = layers.Dropout(0.5)(x) outputs = layers.Dense(self.label_num, activation='softmax')(x) model = keras.Model(inputs, outputs) model.compile(optimizer=keras.optimizers.Adam(learning_rate), loss=self.loss, metrics=self.metrics) # print(model.summary()) return model