基于深度学习的故障诊断：CNN模型训练与优化

概述

本项目使用卷积神经网络 (CNN) 进行故障诊断。我们提供了完整的代码实现，包括数据加载、模型构建、训练和评估等步骤。

代码解析

1. 数据加载与预处理pythonimport sysimport osimport argparseimport numpy as npimport timefrom sklearn.metrics import accuracy_score, confusion_matrixfrom models import CNNfrom mydataset import MyDatasetimport torchfrom torch.utils.data import DataLoaderimport torch.nn as nnimport matplotlib.pyplot as pltimport pandas as pdimport seaborn as sns # µ¼Èë°üimport matplotlib.font_manager as fmfrom tqdm import *import csv

...

def main(args): # »®·ÖÑµÁ·¼¯¡¢²âÊÔ¼¯ train_dataset = MyDataset(args.root, args.txtpath, transform=None) train_loader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0) test_dataset = MyDataset(args.root2, args.txtpath2, transform=None) test_loader = DataLoader(dataset=test_dataset, batch_size=args.batch_size, shuffle=True, num_workers=0)

# ...

这段代码首先导入了必要的库，然后定义了 main 函数，该函数是程序的入口点。在 main 函数中，我们使用 MyDataset 类加载训练集和测试集，并使用 DataLoader 类创建数据加载器。

2. 模型构建与训练python # ...

models = {'CNN': CNN}

# ¶þÕßÈ¡ÆäÒ»    # model = torch.load('./modelpth/40.pth')#,map_location=torch.device('cpu'))  # ¼ÓÔØ²ÎÊý    model = models[args.model]()

if torch.cuda.is_available():        model = model.cuda()    model = torch.load('./modelpth/finalmodel.pth')

optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)    # optimizer.load_state_dict(torch.load('save_optim.pt'))    criterion = nn.CrossEntropyLoss()    batches_per_epoch = int(len(train_dataset) / args.batch_size)    train_loss_list = []    train_acc_list = []    test_loss_list = []    test_acc_list = []    train_time = 0    model_save_path = './modelpth/'    os.makedirs(model_save_path, exist_ok=True)    for epoch in range(50,args.epochs):        # ...        for (cnt, i) in enumerate(tqdm(train_loader)):            # ...            tlabels, tpredi, tloss = train(model, batch_x, batch_y, optimizer, criterion)            # ...

    # ...        acc_score, loss_score, feature_list, C = test(model, test_loader, criterion)        # ...

    if epoch % 1 == 0:            torch.save(model, f'./modelpth/{epoch}.pth')        if epoch == args.epochs - 1:  # ÑµÁ·µ½×îºóÒ»ÂÖ            # ...

这段代码定义了模型、优化器、损失函数等。然后，使用训练集对模型进行训练，并使用测试集评估模型性能。

3. 模型评估与可视化python# ...

def draw(train_acc, train_loss, test_acc, test_loss): #定义一个名为draw的函数，该函数接受四个参数：train_acc、train_loss、test_acc和test_loss。 # ...

def draw_result(C): # ...

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