用pytorch写一段代码:以西储大学CWRU轴承故障的振动信号数据库作为模型的训练集,故障类型标签包括:正常、内圈故障、外圈故障和滚动体故障四类。首先将一维故障信号转换为二维灰度图作为CNN模型的输...
时间: 2024-05-05 17:20:46 浏览: 5
以下是用PyTorch实现将CWRU轴承故障信号转换为灰度图的代码:
``` python
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import ToPILImage, ToTensor, Resize
# 加载数据集
class CWRUDataset(Dataset):
def __init__(self, data_path, label_path):
self.data = np.load(data_path)
self.labels = np.load(label_path)
def __getitem__(self, index):
signal = self.data[index]
label = self.labels[index]
return signal, label
def __len__(self):
return len(self.data)
# 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.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 8 * 8, 256)
self.fc2 = nn.Linear(256, 4)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu(x)
x = self.pool2(x)
x = self.conv3(x)
x = self.relu(x)
x = self.pool3(x)
x = x.view(-1, 64 * 8 * 8)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
# 定义训练函数
def train_model(model, train_loader, criterion, optimizer, num_epochs):
for epoch in range(num_epochs):
running_loss = 0.0
for i, (inputs, labels) in enumerate(train_loader):
inputs = torch.unsqueeze(inputs, dim=1) # 转换为灰度图
inputs = inputs.float()
labels = labels.long()
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
epoch_loss = running_loss / len(train_loader)
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, epoch_loss))
# 加载数据集并进行训练
data_path = 'data.npy'
label_path = 'labels.npy'
dataset = CWRUDataset(data_path, label_path)
train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
num_epochs = 10
train_model(model, train_loader, criterion, optimizer, num_epochs)
```
注意,在训练函数中,我们使用了`torch.unsqueeze`函数将一维故障信号转换为灰度图。同时,我们还需要将输入和标签分别转换为`float`和`long`类型。