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NASA Turbofan Jet Engine Data (Predictive Maintenance)

Industrial IoT
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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_IDCycleOp_Setting_1Sensor_T24Sensor_T30Sensor_T50
11-0.0007641.82554.361400.60
120.0019642.15554.381403.14
13-0.0043642.35554.261404.20

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

Source: Government (2026)

Indexed by IoTDataset.com on Jan 12, 2026

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Related Topics & Keywords

#predictive maintenance #aerospace #turbofan #nasa #sensor fusion #remaining useful life #time series

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