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: 2024-03-10 08:49:24 浏览: 29
好的,以下是一段用 PyTorch 实现的代码,用于将 CWRU 轴承故障信号转化为灰度图并进行 CNN 模型的训练:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np
from PIL import Image
# 定义数据集类
class CWRU_Dataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, index):
# 将一维信号转换为灰度图
img = Image.fromarray(self.data[index].astype(np.uint8)).convert('L')
# 将灰度图转换为 PyTorch Tensor
img = torch.from_numpy(np.array(img)).float()
# 归一化操作
img = img / 255.0
# 添加通道维度
img = img.unsqueeze(0)
return img, self.labels[index]
# 定义 CNN 模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer3 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(4096, 4)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = self.layer3(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
# 加载数据集
data = np.load('data.npy') # 数据集
labels = np.load('labels.npy') # 标签
dataset = CWRU_Dataset(data, labels)
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
# 定义模型、损失函数和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(dataloader):
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, len(dataset)//64, loss.item()))
# 保存模型
torch.save(model.state_dict(), 'cnn_model.pth')
```
需要注意的是,此代码并不包含数据集的处理过程,需要自行下载数据集并对其进行处理。同时,代码中的模型结构和超参数也可以根据具体的需求进行调整。