A Unified Framework for Prognostics and Health Management (PHM) Tasks, enabling streamlined execution of key tasks such as Remaining Useful Life prediction (RUL prediction), fault diagnosis, and anomaly detection.

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

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

Predictive Maintenance on NASA C-MAPSS dataset using LSTM, GRU, and Transformer architectures.

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.