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turbofan-engine

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Transformer implementation with PyTorch for remaining useful life prediction on turbofan engine with NASA CMAPSS data set. Inspired by Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, 1-10.

  • Updated Oct 26, 2021
  • Python

This project builds a machine learning framework for predictive maintenance of turbofan engines, estimating Remaining Useful Life (RUL) from the NASA C-MAPSS sensor dataset. Methods included anomaly detection with CUSUM and autoencoders, and LSTM models, achieving significant RMSE reduction over baselines.

  • Updated Sep 3, 2025
  • Jupyter Notebook

End-to-end ML pipeline for predicting aircraft engine failures using NASA C-MAPSS data. Random Forest model with 171 features achieves RUL prediction for proactive maintenance. Interactive Streamlit dashboard with real-time alerts, predictions analysis, and maintenance recommendations. | Python | scikit-learn | Streamlit

  • Updated Oct 13, 2025
  • Python

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