C-MAPSS Turbofan Engine Degradation Simulation Dataset 核心 · 已核验
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NASA 用 C-MAPSS 仿真器生成的涡扇发动机退化仿真数据集(FD001-FD004 四个子集, 工况与故障模式组合不同),RUL 预测领域引用最多的基准数据集。
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- 落地页
- https://ieee-dataport.org/documents/c-mapss-dataset
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2025
- 发布方
- NASA Prognostics Center of Excellence
- 别名
- C-MAPSS / CMAPSS / NASA Turbofan
- 设备类型
aero_engine- PHM 任务
rul_predictiondegradation_trend_prediction- 跑至失效
- 是
分发点
| ieee_dataport | https://ieee-dataport.org/documents/c-mapss-dataset | |
| phm_society | https://data.phmsociety.org/nasa/ | NASA PCoE 镜像 |
数据概要
文本矩阵(单元号/循环数/3 工况设定/21 传感通道),train/test/RUL 标签三件套
故障工况
| description: 高压压气机(HPC)与风扇模块退化(仿真注入),FD003/004 含双故障模式fault_type: otherinduction: simulated_synthetic |
传感器
| sensor_type: otherobserved_property: temperaturemounting_note: 仿真传感通道(共 21 路:温度/压力/转速等,非物理传感器) |
| sensor_type: otherobserved_property: pressuremounting_note: 仿真传感通道 |
| sensor_type: otherobserved_property: rotating_speedmounting_note: 仿真传感通道(风扇/核心机转速) |
运行工况
| description: 海拔/马赫数/油门解析角组合,FD001/003 单工况,FD002/004 六工况condition_type: otheris_varying: True |
关联论文(1738 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation 2008 · 首发该数据集
- Small data challenges for intelligent prognostics and health management: a review 2024 · 综述收录
- Deep transfer learning in machinery remaining useful life prediction: a systematic review 2024 · 综述收录
- Aero Engine Remaining Useful Life Prediction: A Survey 2023 · 综述收录
- Challenges of machine learning-based RUL prognosis: A review on NASA's C-MAPSS data set 2021 · 综述收录
- Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets 2020 · 综述收录
- Empirical Likelihood Stacking for Remaining Useful Life Estimation 2026 · 用于方法验证
- FusionNet: intelligent sequence fusion for predictive maintenance in edge-enabled industrial IoT systems 2026 · 用于方法验证
- Remaining Useful Life Prediction for Aircraft Maintenance Using Machine Learning 2026 · 使用该数据集
- GASF‐STC: A Gramian Angular Summation Field–Based Spatio‐Temporal Contrastive Network for Remaining Useful Life Prediction 2026 · 用于方法验证
- Engine remaining useful life prediction method based on deep residual network and attention mechanism 2026 · 用于方法验证
- Uncertainty aware predictive maintenance using a hybrid Transformer with Monte Carlo Dropout and conformal prediction 2026 · 用于方法验证
- Fuzzy Inference System-Based Prognostics for Remaining Useful Life Estimation 2026 · 用于方法验证
- Information-theoretic limits of health indicator construction for remaining useful life prediction 2026 · 用于方法验证
- Enhancing prognostic model interpretability for advanced engine failure prediction using prognostic metrics and explainable AI 2026 · 使用该数据集
- Predictive maintenance in aircraft engine maintenance using the C-MAPSS dataset: performance comparison and evaluation of machine learning classification algorithms 2026 · 使用该数据集
- Dynamic rough set learning for reliable early warning in industrial time-series systems 2026 · 用于方法验证
- Emergent detection of concept drift within the glia-inspired ‘rhythmic sharing’ algorithm 2026 · 用于方法验证
- A feature fusion model based on Convolutional Sparse Transformer with convolutional recurrent gated unit for remaining useful life prediction 2026 · 用于方法验证
- Degradation mode identification and remaining useful life prediction via an interpretable CNN-BiLSTM framework 2026 · 用于方法验证
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(9 条)
| 来源链接: https://ieee-dataport.org/documents/c-mapss-dataset 日期: 2026-07-07 |
| 日期: 2026-07-07 |
| 来源链接: https://api.datacite.org/dois/10.21227/q7dr-1b93 日期: 2026-07-08 |
| 日期: 2026-07-09 |
| 日期: 2026-07-11 |
| 日期: 2026-07-11 |
| 日期: 2026-07-25 |
| 日期: 2026-07-26 |
| 日期: 2026-07-29 |