【机器学习】和【人工智能】在制造业领域的应用——案例分析

# 示例代码：使用随机森林进行设备故障预测
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载数据
data = pd.read_csv('machine_sensor_data.csv')

# 特征选择与标签
X = data[['temperature', 'vibration', 'pressure']]
y = data['fault']

# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 预测与评估
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')

# 示例代码：使用卷积神经网络进行产品缺陷检测
import tensorflow as tf
from tensorflow.keras import layers, models

# 加载数据
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()

# 构建CNN模型
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# 编译与训练模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))

# 示例代码：使用时间序列分析进行库存预测
import pandas as pd
from statsmodels.tsa.holtwinters import ExponentialSmoothing

# 加载数据
data = pd.read_csv('sales_data.csv', index_col='date', parse_dates=True)

# 构建模型
model = ExponentialSmoothing(data['sales'], trend='add', seasonal='add', seasonal_periods=12)
fit = model.fit()

# 预测未来销售
forecast = fit.forecast(12)
print(forecast)