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模型的输入,然后用CNN模型训练,做出故障进行正确分类,最后评估模型效果
时间: 2023-03-03 13:54:08 浏览: 114
import torch
import torch.nn as nn
from torch.utils.data import DataLoader# 加载CWRU轴承故障训练数据
cwru_dataset = load_cwru_dataset()# 将一维信号转换为二维灰度图
def convert_1d_to_2d(data):
# ...# 将数据集中的一维信号转换为二维灰度图
cwru_dataset = cwru_dataset.map(convert_1d_to_2d)# 定义CNN网络
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=3, stride=1, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 4) def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x# 定义损失函数及优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(cnn.parameters(), lr=0.001, momentum=0.9)#
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