本项目旨在构建一个高精度、跨域鲁棒性强的智能故障诊断系统，应用于旋转机械（如轴承）的故障检测和分类。项目将复杂的时域振动信号转化为时频图像，并利用卷积神经网络 (CNN) 进行特征提取和诊断。 项目的核心创新点在于深度迁移学习 (Deep Transfer Learning) 的应用，通过在源域（如 CWRU 公开数据集）训练的模型，快速适应和优化目标域（实际工业数据集）的诊断任务，以解决工业数据采集成本高、故障样本稀缺的问题。
☆27Dec 2, 2025Updated 9 months ago
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