RUL prediction for NASA CMAPSS Turbofan Jet Engines

https://github.com/z-devin/JetEngineRUL

This project implements Remaining Useful Life (RUL) prediction for NASA CMAPSS Turbofan Jet Engines using Long Short-Term Memory (LSTM) neural networks based off engine variables such as cycle times, operational settings, and sensor readings. The solution utilizes sliding windows for time-series data processing and Optuna for hyperparameter optimization across multiple datasets.