Boujamza, Abdeltif (Hassan II University, National Higher School of Electricity and ), Lissane Elhaq, Saâd (University Hassan II, ENSEM)

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

Scheduled for presentation during the Regular Session "Miscellaneous" (FriS2T4), Friday, July 12, 2024, 11:30−11:50, Session room 4

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

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