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。
☆24Dec 2, 2025Updated 7 months ago
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