LSTM轴承寿命预测代码

很抱歉，我是一个语言模型，无法为您提供自己编写的代码。但是，下面是一些示例LSTM轴承寿命预测代码，您可以作为参考：

1. 使用Keras框架的LSTM轴承寿命预测代码

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

# Load the dataset
dataset = pd.read_csv('bearing.csv', header=None)
data = dataset.iloc[:, 1:2].values

# Feature scaling
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# Split the data into train and test sets
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train_data, test_data = data[0:train_size, :], data[train_size:len(data), :]

# Convert the input sequence into a matrix
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back - 1):
        a = dataset[i:(i + look_back), 0]
        X.append(a)
        Y.append(dataset[i + look_back, 0])
    return np.array(X), np.array(Y)

look_back = 25 # Number of previous time steps to use as input features
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# Reshape the input to be [samples, time steps, features]
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# Build the LSTM model
model = Sequential()
model.add(LSTM(units=50, input_shape=(look_back, 1)))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mean_squared_error')

# Train the model
model.fit(trainX, trainY, epochs=100, batch_size=32)

# Make predictions on the test data
predictions = model.predict(testX)

# Plot the results
plt.plot(scaler.inverse_transform(testY.reshape(-1, 1)), label='Actual')
plt.plot(scaler.inverse_transform(predictions), label='Predicted')
plt.legend()
plt.show()

2. 使用PyTorch框架的LSTM轴承寿命预测代码

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler

# Load the dataset
dataset = pd.read_csv('bearing.csv', header=None)
data = dataset.iloc[:, 1:2].values

# Feature scaling
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(data)

# Split the data into train and test sets
train_size = int(len(data) * 0.8)
test_size = len(data) - train_size
train_data, test_data = data[0:train_size, :], data[train_size:len(data), :]

# Convert the input sequence into a tensor
def create_dataset(dataset, look_back=1):
    X, Y = [], []
    for i in range(len(dataset) - look_back):
        a = dataset[i:(i + look_back)]
        X.append(a)
        Y.append(dataset[i + look_back])
    return torch.tensor(X), torch.tensor(Y)

look_back = 25 # Number of previous time steps to use as input features
trainX, trainY = create_dataset(train_data, look_back)
testX, testY = create_dataset(test_data, look_back)

# Build the LSTM model
class LSTM(nn.Module):
    def __init__(self, input_size=1, hidden_size=50, output_size=1):
        super().__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.linear = nn.Linear(hidden_size, output_size)
        
    def forward(self, input):
        lstm_out, _ = self.lstm(input.view(len(input), 1, -1))
        prediction = self.linear(lstm_out.view(len(input), -1))
        return prediction[-1]
    
model = LSTM()
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# Train the model
for epoch in range(500):
    optimizer.zero_grad()
    loss = criterion(model(trainX.float()), trainY.float())
    loss.backward()
    optimizer.step()

# Make predictions on the test data
with torch.no_grad():
    predictions = model(testX.float()).numpy()

# Plot the results
testY_unscaled = scaler.inverse_transform(testY.numpy())
predictions_unscaled = scaler.inverse_transform(predictions)
plt.plot(testY_unscaled, label='Actual')
plt.plot(predictions_unscaled, label='Predicted')
plt.legend()
plt.show()