Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Saptarshi Sengupta

Second Advisor

Ching-Seh Wu

Third Advisor

Navrati Saxena


Long Short-term memory (LSTM), Prognostics, CMAPSS, N-CMAPSS, Recurrent Neural Networks, Predictive Analytics, Turbofan Engine Degradation Modeling


Aircraft engines are susceptible to failure at multiple points over their lifespan and need replacement or repairs. The ability to proactively determine how long an engine will function helps avoid fatalities and build a reliable prognostic system. To accomplish this, predictive models are being developed using various approaches like physics-based and data-driven techniques. Physics-based models need huge computing power for simulations and domain knowledge for understanding and implementing the models. Alternatively, if we have substantial data for prediction, data-driven models can be used. In this research, we use data-driven approach for engine Remaining-Useful-Life (RUL) prediction on the NASA Commercial Modular Aero-Propulsion System Simulation (CMAPSS) dataset, and the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset, containing huge number of samples on real-time flight conditions (~several GBs). As the sensor readings are in the form of

time series, deep Recurrent Neural Networks are suitable for predicting the Remaining-Useful- Life (RUL) of an engine, especially Long Short-Term Memory (LSTM) networks. In this paper,

we propose a Bidirectional LSTM model for RUL predictions on the sub-datasets FD001, FD002, FD003, and FD004 of the CMAPSS dataset resulting in RMSE values of 17.60, 29.67, 17.62, and 31.84 respectively. For the N-CMAPSS dataset, on the sub-datasets DS01, DS02, DS03, and DS07, different architectures of LSTM including Vanilla LSTM, Stacked LSTM, and Bidirectional LSTM are explored with the Stacked LSTM performing best for DS01 with RMSE 6.22 and Bidirectional LSTM performing best for DS02, DS03, and DS07 with RMSE values of 9.08, 10.19, and 11.73.

Available for download on Thursday, May 22, 2025