Life Prediction Python Code

Across companies and industries, common barriers to predictive maintenance manifest themselves in four areas:
1. Do we have enough data?
2. Do we have enough failure data?
3. How can we predict failure?
4. How do we build predictive maintenance algorithms?

寿命预测Python代码
2023年5月13日星期六

【注意】
import numpy as np
import pandas as pd

# 模拟数据
np.random.seed(123)
lifetimes = np.random.randint(200, 1000, size=100)
failure_modes = np.random.choice(['fatigue', 'wear', 'contamination'], size=100)

# 保存数据到csv文件
data = pd.DataFrame({
    
    'lifetime': lifetimes, 'failure_mode': failure_modes})
data.to_csv('bearing_life_data.csv', index=False)
需要注意的是，此代码仅作为示例，实际应用需要根据具体数据进行调参和调整。另外，需要准备轴承寿命数据文件bearing_life_data.csv，其格式应该为每一行表示一个时间序列样本，最后一列为对应的寿命值。
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import LSTM, Dense

# 导入数据
data = pd.read_csv('bearing_life_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 数据预处理
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
X = scaler.fit_transform(X)
y = scaler.fit_transform(y.reshape(-1, 1))

# 数据集划分
train_size = int(len(X) * 0.7)
test_size = len(X) - train_size
X_train, X_test = X[:train_size, :], X[train_size:, :]
y_train, y_test = y[:train_size, :], y[train_size:, :]

# 构建LSTM模型
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mean_squared_error')

# 模型训练
model.fit(X_train.reshape((X_train.shape[0], X_train.shape[1], 1)), y_train, epochs=50, batch_size=32)

# 预测结果
predicted_y = model.predict(X_test.reshape((X_test.shape[0], X_test.shape[1], 1)))
predicted_y = scaler.inverse_transform(predicted_y)

# 可视化结果
import matplotlib.pyplot as plt
plt.plot(y_test, color='blue', label='Actual')
plt.plot(predicted_y, color='red', label='Predicted')
plt.legend()
plt.show()