AI-powered predictive maintenance system that estimates the Remaining Useful Life (RUL) of aircraft engines using NASA CMAPSS sensor data, machine learning, and an interactive Streamlit dashboard.
- Remaining Useful Life (RUL) Prediction
- Engine Health Score Calculation
- Maintenance Recommendations
- Sensor Importance Analysis
- Feature Importance Visualization
- Sensor Trend Analysis
- CSV Upload and Prediction
- Interactive Streamlit Dashboard
- Python
- Pandas
- NumPy
- Scikit-Learn
- Matplotlib
- Streamlit
- Joblib
| Model | MAE |
|---|---|
| Random Forest | 29.69 |
| Extra Trees | 29.46 |
| Gradient Boosting | 29.88 |
Extra Trees Regressor
MAE: 29.46
NASA CMAPSS Turbofan Engine Degradation Simulation Dataset
- Engine health monitoring
- RUL prediction
- Health score estimation
- Maintenance recommendation system
- Sensor importance ranking
- Sensor trend visualization
- Upload custom engine data for prediction
pip install -r requirements.txt
streamlit run app.py
Built by Aishwarya Manoj Nair as an end-to-end Machine Learning and Streamlit project.