NASA Turbofan Jet Engine Data (Predictive Maintenance)
Catalog Summary
"Simulated run-to-failure degradation data from turbofan jet engines. This dataset is widely used as the benchmark for predicting Remaining Useful Life (RUL) in industrial systems."
Catalog Notes
Dataset Overview
This dataset consists of multiple multivariate time series generated by the C-MAPSS simulation software. It tracks the degradation of turbofan engines under varying operating conditions.
Key Features
- Sensors: 21 sensor readings (Temperature, Pressure, Fan Speed, etc.).
- Operational Settings: 3 settings that define the engine's working mode.
- Fault Modes: Includes data for HPC (High-Pressure Compressor) degradation and Fan degradation.
Goal
The main objective is to predict the RUL (Remaining Useful Life) of the engine before a failure occurs.
Data Preview
| Unit_ID | Cycle | Op_Setting_1 | Sensor_T24 | Sensor_T30 | Sensor_T50 |
|---|---|---|---|---|---|
| 1 | 1 | -0.0007 | 641.82 | 554.36 | 1400.60 |
| 1 | 2 | 0.0019 | 642.15 | 554.38 | 1403.14 |
| 1 | 3 | -0.0043 | 642.35 | 554.26 | 1404.20 |
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Provided Citation
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. In Proceedings of the 1st International Conference on Prognostics and Health Management (PHM08), Denver CO.
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Source metadata: Government (2026)
Indexed by IoTDataset.com on Jan 12, 2026
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