  This study uses a Long Short-Term Memory (LSTM) network to predict the
remaining useful life (RUL) of jet engines from time-series data, crucial for
aircraft maintenance and safety. The LSTM model's performance is compared with
a Multilayer Perceptron (MLP) on the C-MAPSS dataset from NASA, which contains
jet engine run-to-failure events. The LSTM learns from temporal sequences of
sensor data, while the MLP learns from static data snapshots. The LSTM model
consistently outperforms the MLP in prediction accuracy, demonstrating its
superior ability to capture temporal dependencies in jet engine degradation
patterns. The software for this project is in
https://github.com/AneesPeringal/rul-prediction.git.
