cnn轴承故障诊断pytorch
时间: 2023-07-24 16:13:55 浏览: 159
对于CNN轴承故障诊断的任务,你可以使用PyTorch来实现。以下是一个简单的示例代码,演示了如何使用CNN模型对轴承故障进行分类诊断。
首先,你需要准备数据集。可以使用带有正常和故障样本的轴承图像数据集。确保数据集已经标记好,每个样本都有对应的标签。
接下来,安装PyTorch和其他必要的依赖库:
```python
pip install torch torchvision matplotlib
```
然后,使用以下代码构建并训练CNN模型:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.fc = nn.Linear(32 * 7 * 7, 2) # 最后一层输出为2,表示正常和故障两类
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.conv2(x)
x = self.relu(x)
x = self.maxpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
# 加载数据集
transform = transforms.Compose([
transforms.Grayscale(), # 转为灰度图像
transforms.ToTensor() # 转为Tensor
])
train_dataset = datasets.ImageFolder('path_to_train_dataset', transform=transform)
test_dataset = datasets.ImageFolder('path_to_test_dataset', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# 初始化模型和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)
for epoch in range(num_epochs):
model.train()
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 测试模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print('Accuracy: {:.2f}%'.format(accuracy * 100))
```
请注意,上述代码中的"path_to_train_dataset"和"path_to_test_dataset"需要替换为你的训练集和测试集的路径。
这是一个简单的示例,你可以根据实际情况进行调整和优化。希望对你有所帮助!