Pytorch 框架 基于迁移学习的CWRU滚动轴承故障诊断 ResNet2D （二维数据） CNN1D，ResNet1D（一维数据） 域自适应算法：DDC_DANN（域对抗迁移网络）
·
基于迁移学习的CWRU滚动轴承故障诊断
文章目录
以下文字及代码仅供参考。
基于深度学习的滚动轴承故障诊断方法：Pytorch 框架， ResNet2D （二维数据） CNN1D，ResNet1D（一维数据）可自行选择
域自适应算法：DDC/DANN（域对抗迁移网络）
为了实现基于迁移学习的CWRU滚动轴承故障诊断系统，我们可以结合深度学习模型（如ResNet2D、CNN1D、ResNet1D）和域自适应算法（如DDC或DANN）。以下是一个详细的代码示例，包括数据预处理、模型定义、训练和评估。
1. 安装依赖
确保你已经安装了必要的Python包：
pip install torch torchvision numpy pandas scikit-learn
2. 数据准备
假设你已经有了CWRU滚动轴承数据集，并且已经进行了适当的预处理。这里我们使用PyTorch的数据加载器来处理数据。
数据集划分
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
class BearingDataset(Dataset):
def __init__(self, data_dir, transform=None):
self.data_dir = data_dir
self.transform = transform
self.data = []
self.labels = []
# 加载数据
for label in os.listdir(data_dir):
label_path = os.path.join(data_dir, label)
for file in os.listdir(label_path):
file_path = os.path.join(label_path, file)
data = np.load(file_path)
self.data.append(data)
self.labels.append(int(label))
self.data = np.array(self.data)
self.labels = np.array(self.labels)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
data = self.data[idx]
label = self.labels[idx]
if self.transform:
data = self.transform(data)
return data, label
# 数据集路径
data_dir = '/path/to/data'
# 划分数据集
train_data, test_data, train_labels, test_labels = train_test_split(
dataset.data, dataset.labels, test_size=0.2, random_state=42)
# 创建数据加载器
train_dataset = BearingDataset(data_dir, transform=None)
test_dataset = BearingDataset(data_dir, transform=None)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
3. 模型定义
根据选择的模型架构（ResNet2D、CNN1D、ResNet1D），定义相应的模型。
ResNet2D 示例
import torch.nn as nn
import torchvision.models as models
class ResNet2D(nn.Module):
def __init__(self, num_classes=10):
super(ResNet2D, self).__init__()
self.model = models.resnet18(pretrained=True)
self.model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.fc = nn.Linear(self.model.fc.in_features, num_classes)
self.model.fc = self.fc
def forward(self, x):
return self.model(x)
CNN1D 示例
class CNN1D(nn.Module):
def __init__(self, num_classes=10):
super(CNN1D, self).__init__()
self.conv1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
self.conv2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 128, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool2(F.relu(self.conv2(x)))
x = x.view(-1, 64 * 128)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
ResNet1D 示例
class ResNet1D(nn.Module):
def __init__(self, num_classes=10):
super(ResNet1D, self).__init__()
self.model = models.resnet18(pretrained=True)
self.model.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.fc = nn.Linear(self.model.fc.in_features, num_classes)
self.model.fc = self.fc
def forward(self, x):
return self.model(x)
4. 域自适应算法
这里以DANN为例，实现域自适应网络。
class DANN(nn.Module):
def __init__(self, backbone, num_classes=10):
super(DANN, self).__init__()
self.backbone = backbone
self.classifier = nn.Linear(backbone.fc.in_features, num_classes)
self.domain_classifier = nn.Sequential(
nn.Linear(backbone.fc.in_features, 1024),
nn.ReLU(),
nn.Linear(1024, 2)
)
def forward(self, x, lambda_val=0.1):
features = self.backbone(x)
class_output = self.classifier(features)
domain_output = self.domain_classifier(features)
return class_output, domain_output
5. 训练与评估
import torch.optim as optim
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train(model, device, train_loader, optimizer, epoch, lambda_val=0.1):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
class_output, domain_output = model(data, lambda_val=lambda_val)
class_loss = F.cross_entropy(class_output, target)
domain_target = torch.zeros(len(data)).long().to(device)
domain_loss = F.cross_entropy(domain_output, domain_target)
loss = class_loss + lambda_val * domain_loss
loss.backward()
optimizer.step()
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)[0]
test_loss += F.cross_entropy(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
# 实例化模型
backbone = ResNet2D(num_classes=10)
model = DANN(backbone)
# 移动到设备上
model.to(device)
# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(1, 11):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
5. 训练与评估
import torch.optim as optim
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train(model, device, train_loader, optimizer, epoch, lambda_val=0.1):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
class_output, domain_output = model(data, lambda_val=lambda_val)
class_loss = F.cross_entropy(class_output, target)
domain_target = torch.zeros(len(data)).long().to(device)
domain_loss = F.cross_entropy(domain_output, domain_target)
loss = class_loss + lambda_val * domain_loss
loss.backward()
optimizer.step()
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)[0]
test_loss += F.cross_entropy(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
# 实例化模型
backbone = ResNet2D(num_classes=10)
model = DANN(backbone)
# 移动到设备上
model.to(device)
# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(1, 11):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
6. 运行代码
确保所有部分都正确配置后，运行代码进行训练和测试。
仅供参考，一个完整的框架，包括数据预处理、模型定义、域自适应算法实现以及训练和评估。你可以根据具体需求调整参数和模型结构。
为了继续实现基于迁移学习的CWRU滚动轴承故障诊断系统，我们需要进一步细化代码结构和功能。以下是一个完整的代码示例，包括数据预处理、模型定义、域自适应算法（DANN）以及训练和评估。
1. 安装依赖
确保你已经安装了必要的Python包：
pip install torch torchvision numpy pandas scikit-learn
2. 数据准备
假设你已经有了CWRU滚动轴承数据集，并且已经进行了适当的预处理。这里我们使用PyTorch的数据加载器来处理数据。
数据集划分
import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
class BearingDataset(Dataset):
def __init__(self, data_dir, transform=None):
self.data_dir = data_dir
self.transform = transform
self.data = []
self.labels = []
# 加载数据
for label in os.listdir(data_dir):
label_path = os.path.join(data_dir, label)
for file in os.listdir(label_path):
file_path = os.path.join(label_path, file)
data = np.load(file_path)
self.data.append(data)
self.labels.append(int(label))
self.data = np.array(self.data)
self.labels = np.array(self.labels)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
data = self.data[idx]
label = self.labels[idx]
if self.transform:
data = self.transform(data)
return data, label
# 数据集路径
data_dir = '/path/to/data'
# 划分数据集
train_data, test_data, train_labels, test_labels = train_test_split(
dataset.data, dataset.labels, test_size=0.2, random_state=42)
# 创建数据加载器
train_dataset = BearingDataset(data_dir, transform=None)
test_dataset = BearingDataset(data_dir, transform=None)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
3. 模型定义
根据选择的模型架构（ResNet2D、CNN1D、ResNet1D），定义相应的模型。
ResNet2D 示例
import torch.nn as nn
import torchvision.models as models
class ResNet2D(nn.Module):
def __init__(self, num_classes=10):
super(ResNet2D, self).__init__()
self.model = models.resnet18(pretrained=True)
self.model.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.fc = nn.Linear(self.model.fc.in_features, num_classes)
self.model.fc = self.fc
def forward(self, x):
return self.model(x)
CNN1D 示例
class CNN1D(nn.Module):
def __init__(self, num_classes=10):
super(CNN1D, self).__init__()
self.conv1 = nn.Conv1d(1, 32, kernel_size=3, stride=1, padding=1)
self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
self.conv2 = nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1)
self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 128, 128)
self.fc2 = nn.Linear(128, num_classes)
def forward(self, x):
x = self.pool1(F.relu(self.conv1(x)))
x = self.pool2(F.relu(self.conv2(x)))
x = x.view(-1, 64 * 128)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
ResNet1D 示例
class ResNet1D(nn.Module):
def __init__(self, num_classes=10):
super(ResNet1D, self).__init__()
self.model = models.resnet18(pretrained=True)
self.model.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.fc = nn.Linear(self.model.fc.in_features, num_classes)
self.model.fc = self.fc
def forward(self, x):
return self.model(x)
4. 域自适应算法
这里以DANN为例，实现域自适应网络。
class DANN(nn.Module):
def __init__(self, backbone, num_classes=10):
super(DANN, self).__init__()
self.backbone = backbone
self.classifier = nn.Linear(backbone.fc.in_features, num_classes)
self.domain_classifier = nn.Sequential(
nn.Linear(backbone.fc.in_features, 1024),
nn.ReLU(),
nn.Linear(1024, 2)
)
def forward(self, x, lambda_val=0.1):
features = self.backbone(x)
class_output = self.classifier(features)
domain_output = self.domain_classifier(features)
return class_output, domain_output
5. 训练与评估
import torch.optim as optim
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train(model, device, train_loader, optimizer, epoch, lambda_val=0.1):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
class_output, domain_output = model(data, lambda_val=lambda_val)
class_loss = F.cross_entropy(class_output, target)
domain_target = torch.zeros(len(data)).long().to(device)
domain_loss = F.cross_entropy(domain_output, domain_target)
loss = class_loss + lambda_val * domain_loss
loss.backward()
optimizer.step()
def test(model, device, test_loader):
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
data, target = data.to(device), target.to(device)
output = model(data)[0]
test_loss += F.cross_entropy(output, target).item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
# 实例化模型
backbone = ResNet2D(num_classes=10)
model = DANN(backbone)
# 移动到设备上
model.to(device)
# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 训练模型
for epoch in range(1, 11):
train(model, device, train_loader, optimizer, epoch)
test(model, device, test_loader)
6. 运行代码
确保所有部分都正确配置后，运行代码进行训练和测试。
以上代码提供了一个完整的框架，包括数据预处理、模型定义、域自适应算法实现以及训练和评估。你可以根据具体需求调整参数和模型结构。
7. 目录结构
确保你的目录结构如下：
transfer-learning-fault-diagnosis-pytorch-main/
├── Backbone/
│ ├── CNN1D.py
│ ├── MLPNet.py
│ └── ResNet1D.py
├── checkpoints/
├── datasets/
├── logs/
├── loss/
├── PreprocessData/
│ ├── ind_py
│ ├── CWRU.py
│ └── preprocess.py
├── Utils/
│ ├── classification.py
│ ├── DANN.py
│ ├── DDC.py
│ └── OSDABP.py
└── main.py
8. 主程序
在 main.py 中，你可以整合所有的模块并运行整个流程。
import argparse
import torch
from Backbone.ResNet2D import ResNet2D
from Utils.DANN import DANN
from datasets.BearingDataset import BearingDataset
from torch.utils.data import DataLoader
def parse_args():
parser = argparse.ArgumentParser(description='Transfer Learning Fault Diagnosis')
parser.add_argument('--backbone', type=str, default='ResNet2D', choices=['ResNet1D', 'ResNet2D', 'CNN1D'])
parser.add_argument('--pretrained', action='store_true', help='Use pre-trained model')
parser.add_argument('--save_model', action='store_true', help='Save the model')
parser.add_argument('--data_dir', type=str, default='/path/to/data', help='Path to data directory')
parser.add_argument('--batch_size', type=int, default=32, help
北京人形旗下天工造物具身智能开源社区，聚焦具身天工与慧思开物两大平台
更多推荐
21
0- 0
已为社区贡献4条内容
所有评论(0)