NASA Turbofan Jet Engine Data (Predictive Maintenance)
Abstract
"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."
Description
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 |
Showing first few rows for preview
Cite This Dataset
Government (2026). NASA Turbofan Jet Engine Data (Predictive Maintenance). [Dataset]. Government. https://www.kaggle.com/datasets/behrad3d/nasa-cmaps
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Original source: Government (2026). Visit official page for more details.
Indexed by IoTDataset.com on Jan 12, 2026
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