以IEEE PHM 2012的数据集做轴承寿命预测,写出代码
时间: 2023-06-19 15:02:52 浏览: 75
由于没有提供数据集,我无法提供完整的代码。但是,我可以给出一个大致的代码框架,您可以根据数据集进行修改和补充。
1. 导入所需的库和数据集
```python
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
# 导入数据集
data = pd.read_csv('data.csv')
```
2. 数据预处理
```python
# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data)
# 将数据集转换为有监督学习问题
x_train, y_train = [], []
for i in range(60, len(data)):
x_train.append(scaled_data[i-60:i, 0])
y_train.append(scaled_data[i, 0])
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
```
3. 构建LSTM模型
```python
# 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))
# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')
# 训练模型
model.fit(x_train, y_train, epochs=50, batch_size=32)
```
4. 预测轴承寿命
```python
# 预测轴承寿命
test_data = pd.read_csv('test.csv')
scaled_test_data = scaler.transform(test_data)
x_test = []
for i in range(60, len(test_data)):
x_test.append(scaled_test_data[i-60:i, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predicted_life = model.predict(x_test)
predicted_life = scaler.inverse_transform(predicted_life)
```
5. 可视化结果
```python
# 可视化结果
plt.plot(test_data['time'], test_data['life'], label='Actual Life')
plt.plot(test_data['time'], predicted_life, label='Predicted Life')
plt.xlabel('Time')
plt.ylabel('Life')
plt.legend()
plt.show()
```