NASA Turbofan Engine Remaining Useful Life (RUL) Prediction

Project Summary

This project predicts the Remaining Useful Life (RUL) of turbofan engines using NASA's C-MAPSS dataset. Accurate RUL predictions enable predictive maintenance to reduce downtime and costs in aviation and manufacturing.

Dataset

• NASA C-MAPSS dataset with multi-sensor time series data of engine degradation.
• Includes training and test sets with operational settings and sensor readings.

Methodology

• Data cleaning and feature engineering to extract degradation patterns.
• Applied regression models: Random Forest, Gradient Boosting.
• Evaluated using RMSE and MAE metrics.

Results

• Visualization of predicted vs actual RUL shown below.

How to Run

1. Clone repo
2. Create environment: pip install -r requirements.txt
3. Run notebooks/RUL_timeseries_XGBoost.ipynb for detailed analysis and model training. You can run this via google colab.
4. There is also a modular framework, if you have enough computing power. Run python main.py to run locally without notebook interface
5. Finally there is a streamlit interface to display results run streamlit run app.py

Future Work

• Incorporate deep learning models (LSTM/GRU) for sequence modeling.
• Implement uncertainty quantification.

Technologies

Python, pandas, scikit-learn, matplotlib, Jupyter Notebook

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