LSTM轴承寿命预测代码

以下是使用LSTM神经网络进行轴承寿命预测的代码示例：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 加载数据集
dataset = pd.read_csv('bearing_dataset.csv')
dataset = dataset[['DE_time', 'FE_time']] # 仅选取需要的特征

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)

# 将数据集拆分为训练集和测试集
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# 将数据集转化为监督学习问题
def create_dataset(dataset, look_back=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back), :]
        dataX.append(a)
        dataY.append(dataset[i + look_back, 1])
    return np.array(dataX), np.array(dataY)

look_back = 10 # 定义时间步长
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# 构建LSTM模型
model = Sequential()
model.add(LSTM(4, input_shape=(look_back, 2)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

# 训练模型
model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)

# 模型预测
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# 反归一化
trainPredict = scaler.inverse_transform(np.concatenate((trainX[:,0,0],trainPredict), axis=0).reshape(-1, 1))
trainPredict = trainPredict[look_back-1:]
testPredict = scaler.inverse_transform(np.concatenate((testX[:,0,0],testPredict), axis=0).reshape(-1, 1))
testPredict = testPredict[look_back-1:]

# 计算RMSE
trainScore = np.sqrt(mean_squared_error(train[:,1], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = np.sqrt(mean_squared_error(test[:,1], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

# 绘图展示结果
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back-1:len(trainPredict)+look_back-1, :] = trainPredict
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)-1:len(dataset)-1, :] = testPredict
plt.plot(scaler.inverse_transform(dataset)[:,1])
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

需要注意的是，这里的数据集是一个包含DE_time和FE_time两个特征的csv文件，需要根据实际情况进行修改。此外，代码中使用了Keras库来构建LSTM神经网络模型。