nasa-turbofan-failure-prediction:数据分析和预测建模项目,重点关注涡轮风扇发动机的剩余使用寿命-源码

上传者: 42112894 | 上传时间: 2021-09-15 20:15:09 | 文件大小: 48.83MB | 文件类型: ZIP
NASA涡轮风扇故障预测 这个数据分析/机器学习项目研究了行为变量与故障发生之间的关系(就剩余的发动机循环而言),用于来自NASA研究项目的模拟运行涡扇数据。 该项目从对数据集的探索开始,随后是基于当前引擎读数的引擎剩余使用寿命(RUL)预测模型的开发。 建模技术包括线性回归和神经网络(使用TF-Keras)。 培训数据来自NASA预测中心数据存储库: : 可以在以下找到有关此调查的文章: : 项目目标 分析发动机性能与剩余使用寿命之间的关系。 开发剩余使用寿命的预测模型。 探索数据集 使用Jupyter笔记本浏览数据集,先进行数据质量检查,然后调查可变关系。 数据质量通常非常好,几乎没有数据丢失或数据类型不正确的情况,尽管随附的文档表明某些传感器上存在噪声。 在数据集中可以看到许多变量之间的强线性相关性,为子集预测模型的变量提供了坚实的基础: 许多变量分布是正态或偏态

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