inception10滚动轴承数据集

Inception10滚动轴承数据集是一个用于机器学习和振动分析的数据集，包含了来自四个不同的滚动轴承的振动信号。这些信号包括正常运行状态下的信号以及受到不同程度的内部故障影响的信号，例如内环和外环的裂纹、滚动体的缺陷等。

该数据集共包含1,040个数据文件，每个文件代表一个滚动轴承的振动信号。其中，正常运行状态下的信号有104个，而其他信号则是由于不同程度的故障而产生的。

该数据集旨在帮助研究人员和工程师开发机器学习算法和技术来识别滚动轴承的故障状态，从而提高设备的可靠性和健康状况。

相关问题

inception10滚动轴承故障诊断数据集代码

以下是使用TensorFlow和Keras实现的Inception10模型来进行滚动轴承故障诊断的代码。

首先，我们需要加载数据集。可以在此处下载数据集：https://data.mendeley.com/datasets/bxk2sdx3wy/1

import numpy as np
import os

# 加载数据集
def load_dataset():
    dataset_path = "data/"
    train_X = np.load(os.path.join(dataset_path, "train_X.npy"))
    train_y = np.load(os.path.join(dataset_path, "train_y.npy"))
    test_X = np.load(os.path.join(dataset_path, "test_X.npy"))
    test_y = np.load(os.path.join(dataset_path, "test_y.npy"))
    return train_X, train_y, test_X, test_y

接下来，我们需要构建Inception10模型。在这里，我们使用了Inception模块，这是Google在2014年提出的一种卷积神经网络结构。

from tensorflow.keras import layers, models

# 构建Inception模块
def inception_module(inputs, filters):
    path1 = layers.Conv2D(filters=filters[0], kernel_size=1, activation="relu")(inputs)

    path2 = layers.Conv2D(filters=filters[1], kernel_size=1, activation="relu")(inputs)
    path2 = layers.Conv2D(filters=filters[2], kernel_size=3, padding="same", activation="relu")(path2)

    path3 = layers.Conv2D(filters=filters[3], kernel_size=1, activation="relu")(inputs)
    path3 = layers.Conv2D(filters=filters[4], kernel_size=5, padding="same", activation="relu")(path3)

    path4 = layers.MaxPooling2D(pool_size=3, strides=1, padding="same")(inputs)
    path4 = layers.Conv2D(filters=filters[5], kernel_size=1, activation="relu")(path4)

    return layers.concatenate([path1, path2, path3, path4])

# 构建Inception10模型
def build_inception10(input_shape, num_classes):
    inputs = layers.Input(shape=input_shape)

    x = layers.Conv2D(filters=64, kernel_size=7, strides=2, padding="same", activation="relu")(inputs)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    x = layers.Conv2D(filters=64, kernel_size=1, activation="relu")(x)
    x = layers.Conv2D(filters=192, kernel_size=3, padding="same", activation="relu")(x)
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    x = inception_module(x, filters=[64, 96, 128, 16, 32, 32])
    x = inception_module(x, filters=[128, 128, 192, 32, 96, 64])
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    x = inception_module(x, filters=[192, 96, 208, 16, 48, 64])
    x = inception_module(x, filters=[160, 112, 224, 24, 64, 64])
    x = inception_module(x, filters=[128, 128, 256, 24, 64, 64])
    x = inception_module(x, filters=[112, 144, 288, 32, 64, 64])
    x = inception_module(x, filters=[256, 160, 320, 32, 128, 128])
    x = layers.MaxPooling2D(pool_size=3, strides=2, padding="same")(x)

    x = inception_module(x, filters=[256, 160, 320, 32, 128, 128])
    x = inception_module(x, filters=[384, 192, 384, 48, 128, 128])

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dropout(0.4)(x)

    outputs = layers.Dense(num_classes, activation="softmax")(x)

    return models.Model(inputs, outputs)

然后，我们可以定义一些超参数并训练模型。

from tensorflow.keras import optimizers, losses, metrics
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping

# 定义超参数
input_shape = (64, 64, 1)
num_classes = 4
batch_size = 128
epochs = 50

# 加载数据集
train_X, train_y, test_X, test_y = load_dataset()

# 构建模型
model = build_inception10(input_shape, num_classes)

# 编译模型
model.compile(optimizer=optimizers.Adam(), loss=losses.sparse_categorical_crossentropy, metrics=[metrics.sparse_categorical_accuracy])

# 定义回调函数
callbacks = [
    ModelCheckpoint("inception10.h5", save_best_only=True),
    EarlyStopping(patience=5, restore_best_weights=True)
]

# 训练模型
history = model.fit(train_X, train_y, batch_size=batch_size, epochs=epochs, validation_data=(test_X, test_y), callbacks=callbacks)

# 评估模型
loss, accuracy = model.evaluate(test_X, test_y)
print("Test loss: {:.4f}, accuracy: {:.4f}".format(loss, accuracy))

最后，我们可以使用模型进行预测。

# 使用模型进行预测
predictions = model.predict(test_X)

# 输出预测结果
print(predictions)

inception滚动轴承故障诊断数据集代码

以下是一个使用Inception网络进行滚动轴承故障诊断的代码示例，使用的数据集是一个经过处理的滚动轴承故障数据集（包含正常和故障样本）。

首先，我们需要导入必要的库：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, Dense, Flatten
from tensorflow.keras.models import Model

然后，定义输入数据的形状和类别数：

input_shape = (64, 64, 1)
num_classes = 10

接下来，定义Inception网络的架构和参数：

def InceptionV1(input_shape, num_classes):
    input_layer = Input(shape=input_shape)

    # Block 1
    conv1_7x7_s2 = Conv2D(64, (7, 7), strides=(2, 2), padding='same', activation='relu', name='conv1/7x7_s2')(input_layer)
    pool1_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool1/3x3_s2')(conv1_7x7_s2)
    pool1_norm1 = tf.nn.local_response_normalization(pool1_3x3_s2, depth_radius=2, alpha=2e-05, beta=0.75, name='pool1/norm1')

    # Block 2
    conv2_3x3_reduce = Conv2D(64, (1, 1), padding='same', activation='relu', name='conv2/3x3_reduce')(pool1_norm1)
    conv2_3x3 = Conv2D(192, (3, 3), padding='same', activation='relu', name='conv2/3x3')(conv2_3x3_reduce)
    conv2_norm2 = tf.nn.local_response_normalization(conv2_3x3, depth_radius=2, alpha=2e-05, beta=0.75, name='conv2/norm2')
    pool2_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool2/3x3_s2')(conv2_norm2)

    # Inception 3a
    inception_3a_1x1 = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_3a/1x1')(pool2_3x3_s2)
    inception_3a_3x3_reduce = Conv2D(96, (1, 1), padding='same', activation='relu', name='inception_3a/3x3_reduce')(pool2_3x3_s2)
    inception_3a_3x3 = Conv2D(128, (3, 3), padding='same', activation='relu', name='inception_3a/3x3')(inception_3a_3x3_reduce)
    inception_3a_5x5_reduce = Conv2D(16, (1, 1), padding='same', activation='relu', name='inception_3a/5x5_reduce')(pool2_3x3_s2)
    inception_3a_5x5 = Conv2D(32, (5, 5), padding='same', activation='relu', name='inception_3a/5x5')(inception_3a_5x5_reduce)
    inception_3a_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_3a/pool')(pool2_3x3_s2)
    inception_3a_pool_proj = Conv2D(32, (1, 1), padding='same', activation='relu', name='inception_3a/pool_proj')(inception_3a_pool)
    inception_3a_output = tf.keras.layers.concatenate([inception_3a_1x1, inception_3a_3x3, inception_3a_5x5, inception_3a_pool_proj], axis=3, name='inception_3a/output')

    # Inception 3b
    inception_3b_1x1 = Conv2D(128, (1, 1), padding='same', activation='relu', name='inception_3b/1x1')(inception_3a_output)
    inception_3b_3x3_reduce = Conv2D(128, (1, 1), padding='same', activation='relu', name='inception_3b/3x3_reduce')(inception_3a_output)
    inception_3b_3x3 = Conv2D(192, (3, 3), padding='same', activation='relu', name='inception_3b/3x3')(inception_3b_3x3_reduce)
    inception_3b_5x5_reduce = Conv2D(32, (1, 1), padding='same', activation='relu', name='inception_3b/5x5_reduce')(inception_3a_output)
    inception_3b_5x5 = Conv2D(96, (5, 5), padding='same', activation='relu', name='inception_3b/5x5')(inception_3b_5x5_reduce)
    inception_3b_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_3b/pool')(inception_3a_output)
    inception_3b_pool_proj = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_3b/pool_proj')(inception_3b_pool)
    inception_3b_output = tf.keras.layers.concatenate([inception_3b_1x1, inception_3b_3x3, inception_3b_5x5, inception_3b_pool_proj], axis=3, name='inception_3b/output')

    # Inception 4a
    inception_4a_1x1 = Conv2D(192, (1, 1), padding='same', activation='relu', name='inception_4a/1x1')(inception_3b_output)
    inception_4a_3x3_reduce = Conv2D(96, (1, 1), padding='same', activation='relu', name='inception_4a/3x3_reduce')(inception_3b_output)
    inception_4a_3x3 = Conv2D(208, (3, 3), padding='same', activation='relu', name='inception_4a/3x3')(inception_4a_3x3_reduce)
    inception_4a_5x5_reduce = Conv2D(16, (1, 1), padding='same', activation='relu', name='inception_4a/5x5_reduce')(inception_3b_output)
    inception_4a_5x5 = Conv2D(48, (5, 5), padding='same', activation='relu', name='inception_4a/5x5')(inception_4a_5x5_reduce)
    inception_4a_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_4a/pool')(inception_3b_output)
    inception_4a_pool_proj = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_4a/pool_proj')(inception_4a_pool)
    inception_4a_output = tf.keras.layers.concatenate([inception_4a_1x1, inception_4a_3x3, inception_4a_5x5, inception_4a_pool_proj], axis=3, name='inception_4a/output')

    # Inception 4b
    inception_4b_1x1 = Conv2D(160, (1, 1), padding='same', activation='relu', name='inception_4b/1x1')(inception_4a_output)
    inception_4b_3x3_reduce = Conv2D(112, (1, 1), padding='same', activation='relu', name='inception_4b/3x3_reduce')(inception_4a_output)
    inception_4b_3x3 = Conv2D(224, (3, 3), padding='same', activation='relu', name='inception_4b/3x3')(inception_4b_3x3_reduce)
    inception_4b_5x5_reduce = Conv2D(24, (1, 1), padding='same', activation='relu', name='inception_4b/5x5_reduce')(inception_4a_output)
    inception_4b_5x5 = Conv2D(64, (5, 5), padding='same', activation='relu', name='inception_4b/5x5')(inception_4b_5x5_reduce)
    inception_4b_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_4b/pool')(inception_4a_output)
    inception_4b_pool_proj = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_4b/pool_proj')(inception_4b_pool)
    inception_4b_output = tf.keras.layers.concatenate([inception_4b_1x1, inception_4b_3x3, inception_4b_5x5, inception_4b_pool_proj], axis=3, name='inception_4b/output')

    # Inception 4c
    inception_4c_1x1 = Conv2D(128, (1, 1), padding='same', activation='relu', name='inception_4c/1x1')(inception_4b_output)
    inception_4c_3x3_reduce = Conv2D(128, (1, 1), padding='same', activation='relu', name='inception_4c/3x3_reduce')(inception_4b_output)
    inception_4c_3x3 = Conv2D(256, (3, 3), padding='same', activation='relu', name='inception_4c/3x3')(inception_4c_3x3_reduce)
    inception_4c_5x5_reduce = Conv2D(24, (1, 1), padding='same', activation='relu', name='inception_4c/5x5_reduce')(inception_4b_output)
    inception_4c_5x5 = Conv2D(64, (5, 5), padding='same', activation='relu', name='inception_4c/5x5')(inception_4c_5x5_reduce)
    inception_4c_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_4c/pool')(inception_4b_output)
    inception_4c_pool_proj = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_4c/pool_proj')(inception_4c_pool)
    inception_4c_output = tf.keras.layers.concatenate([inception_4c_1x1, inception_4c_3x3, inception_4c_5x5, inception_4c_pool_proj], axis=3, name='inception_4c/output')

    # Inception 4d
    inception_4d_1x1 = Conv2D(112, (1, 1), padding='same', activation='relu', name='inception_4d/1x1')(inception_4c_output)
    inception_4d_3x3_reduce = Conv2D(144, (1, 1), padding='same', activation='relu', name='inception_4d/3x3_reduce')(inception_4c_output)
    inception_4d_3x3 = Conv2D(288, (3, 3), padding='same', activation='relu', name='inception_4d/3x3')(inception_4d_3x3_reduce)
    inception_4d_5x5_reduce = Conv2D(32, (1, 1), padding='same', activation='relu', name='inception_4d/5x5_reduce')(inception_4c_output)
    inception_4d_5x5 = Conv2D(64, (5, 5), padding='same', activation='relu', name='inception_4d/5x5')(inception_4d_5x5_reduce)
    inception_4d_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_4d/pool')(inception_4c_output)
    inception_4d_pool_proj = Conv2D(64, (1, 1), padding='same', activation='relu', name='inception_4d/pool_proj')(inception_4d_pool)
    inception_4d_output = tf.keras.layers.concatenate([inception_4d_1x1, inception_4d_3x3, inception_4d_5x5, inception_4d_pool_proj], axis=3, name='inception_4d/output')

    # Inception 4e
    inception_4e_1x1 = Conv2D(256, (1, 1), padding='same', activation='relu', name='inception_4e/1x1')(inception_4d_output)
    inception_4e_3x3_reduce = Conv2D(160, (1, 1), padding='same', activation='relu', name='inception_4e/3x3_reduce')(inception_4d_output)
    inception_4e_3x3 = Conv2D(320, (3, 3), padding='same', activation='relu', name='inception_4e/3x3')(inception_4e_3x3_reduce)
    inception_4e_5x5_reduce = Conv2D(32, (1, 1), padding='same', activation='relu', name='inception_4e/5x5_reduce')(inception_4d_output)
    inception_4e_5x5 = Conv2D(128, (5, 5), padding='same', activation='relu', name='inception_4e/5x5')(inception_4e_5x5_reduce)
    inception_4e_pool = MaxPooling2D(pool_size=(3, 3), strides=(1, 1), padding='same', name='inception_4e/pool')(inception_4d_output)
    inception_4e_pool_proj = Conv2D(128, (1, 1), padding='same', activation='relu', name='inception_4e/pool_proj')(inception_4e_pool)
    inception_4e_output = tf.keras.layers.concatenate([inception_4e_1x1, inception_4e_3x3, inception_4e_5x5, inception_4e_pool_proj], axis=3, name='inception_4e/output')

    # Pooling and Dropout layers
    pool4_3x3_s2 = MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='pool4/3x3_s2')(inception_4e_output)
    dropout_layer1 = Dropout(0.4)(pool4_3x3_s2)
    flatten_layer = Flatten()(dropout_layer1)
    dense_layer1 = Dense(1024, activation='relu')(flatten_layer)
    dropout_layer2 = Dropout(0.4)(dense_layer1)

    # Output layer
    output_layer = Dense(num_classes, activation='softmax')(dropout_layer2)

    # Compile the model
    model = Model(inputs=input_layer, outputs=output_layer)
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

    return model

model = InceptionV1(input_shape, num_classes)
model.summary()

接下来，我们可以使用该模型在数据集上进行训练和评估：

# Load the dataset
train_data = tf.keras.preprocessing.image_dataset_from_directory(
    'path/to/dataset',
    validation_split=0.2,
    subset='training',
    seed=123,
    image_size=(64, 64),
    batch_size=32)

test_data = tf.keras.preprocessing.image_dataset_from_directory(
    'path/to/dataset',
    validation_split=0.2,
    subset='validation',
    seed=123,
    image_size=(64, 64),
    batch_size=32)

# Train the model
history = model.fit(
    train_data,
    validation_data=test_data,
    epochs=10
)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_data)
print('Test accuracy:', test_acc)

这是一个基本的使用Inception网络进行滚动轴承故障诊断的代码示例。您可能需要根据自己的数据集和问题进行修改和调整。