Optimizing Remaining Useful Life Predictions for Aircraft Engines: A Dilated Recurrent Neural Network Approach

Abstract

Predicting the remaining useful life (RUL) plays a crucial rule in the field of prognostics and health management (PHM) for mechanical systems. Specifically within the domain of turbofan engines, predicting RUL plays a vital role in strategically planning maintenance activities. Consequently this aids in optimizing the overall performance of the energy system by reducing downtime and improving sustainability and efficiency. This research endeavors to forecast the RUL of turbofan engines. It employs a Dilated Recurrent Neural Network (D-RNN) Approach, a neural network structure that integrates dilated convolutions into the recurrent layers. The model underwent fine-tuning through a random grid search optimization and was tested using the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset. The results showcase the superior performance of the proposed D-RNN, outperforming the accuracy of other research studies.