一维cnn轴承故障诊断torch
时间: 2023-09-24 07:04:55 浏览: 96
对于一维CNN轴承故障诊断,可以使用PyTorch来实现。以下是一个示例代码,可以用于训练和测试一个基本的一维CNN模型来诊断轴承故障。
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# 定义一维CNN模型
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, self).__init__()
self.conv1 = nn.Conv1d(in_channels=1, out_channels=16, kernel_size=3)
self.relu = nn.ReLU()
self.pool = nn.MaxPool1d(kernel_size=2)
self.fc = nn.Linear(16, 2)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.pool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
# 定义训练集和测试集的数据加载器
class BearingDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __getitem__(self, index):
return self.data[index], self.labels[index]
def __len__(self):
return len(self.data)
# 定义训练函数
def train(model, train_loader, criterion, optimizer, device):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
return epoch_loss
# 定义测试函数
def test(model, test_loader, criterion, device):
model.eval()
running_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
running_loss += loss.item() * inputs.size(0)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
epoch_loss = running_loss / len(test_loader.dataset)
accuracy = correct / total
return epoch_loss, accuracy
# 加载数据并进行预处理
train_data = torch.Tensor(...) # 训练集数据,维度为 [样本数, 特征数, 信号长度]
train_labels = torch.LongTensor(...) # 训练集标签,维度为 [样本数]
test_data = torch.Tensor(...) # 测试集数据,维度为 [样本数, 特征数, 信号长度]
test_labels = torch.LongTensor(...) # 测试集标签,维度为 [样本数]
train_dataset = BearingDataset(train_data, train_labels)
test_dataset = BearingDataset(test_data, test_labels)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 定义设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 创建模型实例
model = CNNModel()
model.to(device)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练和测试模型
num_epochs = 10
for epoch in range(num_epochs):
train_loss = train(model, train_loader, criterion, optimizer, device)
test_loss, test_accuracy = test(model, test_loader, criterion, device)
print(f"Epoch {epoch+1}/{num_epochs} - Train Loss: {train_loss:.4f} - Test Loss: {test_loss:.4f} - Test Accuracy: {test_accuracy:.4f}")
```
请注意,上述代码只是一个简单的示例,你需要根据你的数据和问题适当进行修改和调整。
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