LSTM进行轴承剩余寿命预测代码

以下是一个LSTM进行轴承剩余寿命预测的简单示例代码：

# 导入必要的库
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.preprocessing import MinMaxScaler

# 读取数据
df = pd.read_csv('bearing_data.csv')

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
df['scaled_rms'] = scaler.fit_transform(df['rms'].values.reshape(-1,1))

# 定义函数以生成训练和测试数据
def create_dataset(dataset, lookback):
    X, Y = [], []
    for i in range(len(dataset)-lookback-1):
        a = dataset[i:(i+lookback), 0]
        X.append(a)
        Y.append(dataset[i + lookback, 0])
    return np.array(X), np.array(Y)

# 准备训练和测试数据
lookback = 5
train_size = int(len(df) * 0.7)
train, test = df.iloc[0:train_size], df.iloc[train_size:len(df)]
X_train, Y_train = create_dataset(train['scaled_rms'].values.reshape(-1,1), lookback)
X_test, Y_test = create_dataset(test['scaled_rms'].values.reshape(-1,1), lookback)

# 转换数据为LSTM可接受的格式
X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))
X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))

# 构建模型
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(X_train, Y_train, epochs=100, batch_size=32, validation_data=(X_test, Y_test), verbose=2, shuffle=False)

# 预测数据
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# 反转换数据
train_predict = scaler.inverse_transform(train_predict)
Y_train = scaler.inverse_transform([Y_train])
test_predict = scaler.inverse_transform(test_predict)
Y_test = scaler.inverse_transform([Y_test])

# 计算误差
train_score = math.sqrt(mean_squared_error(Y_train[0], train_predict[:,0]))
test_score = math.sqrt(mean_squared_error(Y_test[0], test_predict[:,0]))
print('Train Score: %.2f RMSE' % (train_score))
print('Test Score: %.2f RMSE' % (test_score))

其中，`bearing_data.csv` 是包含轴承数据的CSV文件，`lookback` 是LSTM使用的时间步数，`train_size` 是用于训练模型的数据集大小。该代码会输出训练和测试的RMSE误差。