编写基于DCGAN的滚动轴承故障诊断代码

首先，需要准备一个包含滚动轴承正常和故障状态的数据集。可以使用一些常见的故障类型，如内圈故障、外圈故障、滚动体故障等。

接下来，我们可以使用DCGAN来训练一个生成器，使其能够生成与正常和故障状态相似的滚动轴承图像。以下是基于PyTorch的DCGAN示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torchvision.utils as vutils

# 定义生成器
class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(64 * 8),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 4),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 2),
            nn.ReLU(True),
            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
            nn.ConvTranspose2d(64, 1, 4, 2, 1, bias=False),
            nn.Tanh()
        )

    def forward(self, input):
        return self.main(input)

# 定义鉴别器
class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(
            nn.Conv2d(1, 64, 4, 2, 1, bias=False),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 64 * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 2),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64 * 2, 64 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 4),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64 * 4, 64 * 8, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64 * 8),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64 * 8, 1, 4, 1, 0, bias=False),
            nn.Sigmoid()
        )

    def forward(self, input):
        return self.main(input)

# 初始化参数
ngpu = 1
nz = 100
ngf = 64
ndf = 64
lr = 0.0002
beta1 = 0.5
batch_size = 64
num_epochs = 50

# 加载数据集
transform = transforms.Compose([
    transforms.Grayscale(1),
    transforms.Resize(64),
    transforms.CenterCrop(64),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
dataset = datasets.ImageFolder('path/to/dataset', transform=transform)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 初始化网络
netG = Generator(ngpu).cuda()
netD = Discriminator(ngpu).cuda()

# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))

# 训练模型
for epoch in range(num_epochs):
    for i, data in enumerate(dataloader, 0):
        # 训练鉴别器
        netD.zero_grad()
        real = data[0].cuda()
        b_size = real.size(0)
        label = torch.full((b_size,), 1, dtype=torch.float, device='cuda')
        output = netD(real).view(-1)
        errD_real = criterion(output, label)
        errD_real.backward()
        D_x = output.mean().item()

        noise = torch.randn(b_size, nz, 1, 1, device='cuda')
        fake = netG(noise)
        label.fill_(0)
        output = netD(fake.detach()).view(-1)
        errD_fake = criterion(output, label)
        errD_fake.backward()
        D_G_z1 = output.mean().item()
        errD = errD_real + errD_fake
        optimizerD.step()

        # 训练生成器
        netG.zero_grad()
        label.fill_(1)
        output = netD(fake).view(-1)
        errG = criterion(output, label)
        errG.backward()
        D_G_z2 = output.mean().item()
        optimizerG.step()

        # 打印损失信息
        if i % 100 == 0:
            print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
                  % (epoch, num_epochs, i, len(dataloader),
                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))

    # 保存生成器的输出图片
    if epoch % 10 == 0:
        vutils.save_image(fake.detach(), 'output/fake_samples_epoch_%03d.png' % epoch, normalize=True)

# 保存模型参数
torch.save(netG.state_dict(), 'netG.pth')
torch.save(netD.state_dict(), 'netD.pth')

训练完成后，我们可以使用生成器来生成一些滚动轴承图像，并使用鉴别器来判断它们是否为正常或故障状态。如果生成的图像与故障状态相似，则可以诊断出故障。