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

Small environment for debugging JS code.

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.

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

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