NASA C-MAPSS Turbofan Engine Degradation — 4 Sub-datasets, 21 Sensors [Run-to-Failure]
Catalog Summary
"NASA Prognostics Center run-to-failure simulation dataset for turbofan engines. Four operational sub-datasets with 21 sensor channels and 3 operational settings. TXT/CSV format. Primary benchmark for Remaining Useful Life (RUL) estimation."
Catalog Notes
Overview
The C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) Turbofan Engine Degradation Simulation Dataset is one of the most widely used public benchmarks in industrial predictive maintenance. Released by the NASA Ames Prognostics Center of Excellence (PCoE), it simulates run-to-failure degradation trajectories for fleets of turbofan jet engines under varying operational conditions and fault modes.
The dataset is divided into four sub-challenges (FD001–FD004) of increasing complexity, reflecting one or two fault modes and one or six operational conditions per challenge. In the training set, engines run from normal operation to failure. In the test set, the time series ends at some point before failure, and the goal is to predict the Remaining Useful Life (RUL) for each engine unit.
Each record captures 21 sensor readings including fan inlet temperature, LPC outlet temperature, HPC outlet temperature, LPC outlet pressure, physical fan speed, corrected fan speed, bypass ratio, and bleed enthalpy, among others. C-MAPSS remains the standard RUL benchmark in industrial IoT, deep learning, and prognostics research.
Column Schema
| Column | Description |
|---|---|
| unit_id | Engine unit number identifier. |
| time_cycles | Operational time in engine cycles. |
| op_setting_1 | Operational setting 1 (one of 3 settings). |
| op_setting_2 | Operational setting 2. |
| op_setting_3 | Operational setting 3. |
| sensor_1 … sensor_21 | 21 sensor measurement channels (temperature, pressure, speed, etc.). |
| RUL | Remaining Useful Life label (provided in separate RUL file for test set). |
Key Statistics
- Sub-datasets: FD001, FD002, FD003, FD004
- Features: 26 columns (unit ID, time, 3 op settings, 21 sensors)
- Fault Modes: 1 or 2 per sub-dataset
- Operational Conditions: 1 or 6 per sub-dataset
- File Format: TXT (space-delimited, CSV-compatible)
- Time Period: Released 2008
Use Cases
- Remaining Useful Life (RUL) estimation and prognostics modelling
- Deep learning benchmarking with LSTM, CNN, Transformer architectures
- Anomaly detection and fault mode classification in industrial engines
- Transfer learning across operational conditions for IIoT systems
Source & Attribution
Created by Abhinav Saxena and Kai Goebel at the NASA Ames Prognostics Center of Excellence. The dataset is hosted on NASA's Open Data Portal and also mirrored on Zenodo. It is cited in thousands of prognostics and health management publications worldwide.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Government.
Preview on GovernmentCite This Dataset
Saxena, A., & Goebel, K. (2008). CMAPSS Jet Engine Simulated Data. [Dataset]. NASA Ames Prognostics Center of Excellence / NASA Open Data Portal. https://data.nasa.gov/dataset/cmapss-jet-engine-simulated-data
Source metadata: NASA Ames Prognostics Center of Excellence / NASA Open Data Portal (2008)
Indexed by IoTDataset.com on Apr 10, 2026
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