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: 2024-05-12 10:15:20 浏览: 16
以下是一个简单的示例代码,用于将一维振动信号转换为二维灰度图像并使用CNN模型进行分类训练:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from scipy.io import loadmat
from skimage.transform import resize
# 加载CWRU轴承故障数据集
data = loadmat('path/to/data.mat')
# 获取振动信号数据和标签
signals = data['X']
labels = data['Y'].ravel()
# 定义图像大小
image_size = (64, 64)
# 将每个信号转换为灰度图像
images = []
for i in range(len(signals)):
# 标准化信号数据
signal = signals[i] / np.max(np.abs(signals[i]))
# 将信号数据调整为图像大小
image = resize(signal, image_size, anti_aliasing=True)
# 将图像转换为灰度图像
image = np.expand_dims(image, axis=2)
image = np.repeat(image, 3, axis=2)
images.append(image)
# 将图像数据转换为张量
images = np.array(images)
images = torch.Tensor(images)
# 将标签数据转换为张量
labels = torch.LongTensor(labels)
# 定义CNN模型
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, 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)
def forward(self, x):
x = self.pool1(torch.relu(self.conv1(x)))
x = self.pool2(torch.relu(self.conv2(x)))
x = self.pool3(torch.relu(self.conv3(x)))
x = x.view(-1, 64 * 8 * 8)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# 实例化模型和损失函数
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for i in range(len(images)):
# 将图像和标签数据加载到GPU上
inputs = images[i].unsqueeze(0).cuda()
labels = labels[i].unsqueeze(0).cuda()
# 将梯度缓存清零
optimizer.zero_grad()
# 前向传播、反向传播和优化
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 打印统计信息
running_loss += loss.item()
if i % 100 == 99:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
```
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