Unified PHM framework for Remaining Useful Life (RUL) prediction, fault diagnosis, fault detection, and anomaly detection for bearings, turbofan engines, and other industrial systems.

Gas Turbine / Engine Deck in Python for use in flight test.

End-to-end predictive maintenance pipeline on NASA C-MAPSS with LSTM, GRU, Transformer, benchmarking, and automated reporting.

The project aims to study the parametrical parameters of a two-spool turbofan with convergent nozzles

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.

Small environment for debugging JS code.

Predictive maintenance for aircraft turbofan engines, RUL prediction using ML & LSTM on NASA CMAPSS dataset

End-to-end predictive maintenance pipeline on the NASA CMAPSS turbofan dataset (Python, scikit-learn, XGBoost, Plotly Dash).

Official code for arXiv:2604.13459 - Asymmetric-Loss CNN-BiLSTM-Attention for Industrial RUL Prediction

Production-ready turbofan predictive maintenance platform using time-series analysis and deep learning to forecast NASA CMAPSS engine remaining useful life (RUL), with feature engineering, model evaluation, FastAPI APIs, and interactive monitoring dashboards.