本项目致力于解决预测性维护 (PdM) 领域的核心问题：剩余使用寿命 (RUL) 预测。我们使用 NASA 著名的 C-MAPSS (涡轮风扇发动机退化模拟) 数据集，实现了一个先进的深度学习模型。 与传统的 RNN/LSTM 方法不同，本项目采用了一种混合模型架构 (Conv1D + Transformer)，旨在同时捕捉时间序列数据中的局部特征（通过1D卷积层平滑噪音、提取模式）和全局依赖关系（通过 Transformer 编码器）。 该模型在模块化的 Python 脚本中实现，易于训练、评估和部署，并为 C-MAPSS 数据集中复杂的多工况 (Multi-Operating Condition) 和多故障 (Multi-Fault) 场景提供了强大的解决方案。
☆27Dec 17, 2025Updated 7 months ago
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