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...

以下是一个简单的示例代码，用于将一维信号转换为二维灰度图，并使用PyTorch中的CNN模型进行训练。

首先，需要导入必要的Python库和CWRU轴承故障的振动信号数据库。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

from sklearn.model_selection import train_test_split

# Load the CWRU bearing dataset
data = pd.read_csv("cwru_data.csv")
labels = pd.read_csv("cwru_labels.csv")

然后，我们将一维信号转换为二维灰度图。我们可以使用Python的OpenCV库进行这个过程，也可以手动编写代码来完成这个过程。这里，我们演示手动编写代码的过程。

# Convert 1D signals to 2D grayscale images
def signal_to_image(signal):
    image = np.zeros((256, 256))
    for i in range(256):
        for j in range(256):
            image[i][j] = signal[i*256+j]
    return image

# Convert all signals to images
images = []
for i in range(len(data)):
    images.append(signal_to_image(data.iloc[i]))

# Convert images to PyTorch tensors
images = torch.tensor(images).unsqueeze(1)

接下来，我们需要将故障类型标签转换为数字标签。

# Convert labels to numerical labels
label_map = {"Normal": 0, "Inner": 1, "Outer": 2, "Rolling": 3}
labels = labels.replace({"Fault": label_map})

然后，我们可以将数据集拆分为训练集和测试集。

# Split the dataset into train and test sets
train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.2, random_state=42)

最后，我们可以定义CNN模型并进行训练。

# Define the CNN model
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc1 = nn.Linear(64 * 64 * 64, 128)
        self.fc2 = nn.Linear(128, 4)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = x.view(-1, 64 * 64 * 64)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the model and loss function
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 200 == 199:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 200))
            running_loss = 0.0

# Test the model
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

这里，我们使用了一个简单的CNN模型，并在训练集上进行了10个epoch的训练。最后，我们在测试集上评估了模型的准确性。