Transformer-based dual-input deep learning framework for remaining useful life prediction using sensor fusion and attention mechanism

Abstract

Accurate prediction of the remaining useful life (RUL) of aircraft engines is critical for predictive maintenance and operational reliability. It enables airlines to plan maintenance operations, thereby avoiding unexpected failures and unnecessary unscheduled downtime. Through the use of sensor signals and complex deep learning models, RUL estimation ensures safety, maximizes engine utilization, and saves on maintenance costs. This work addresses the fundamental problem of precisely forecasting the RUL of aircraft engines based on NASA’s Commercial Modular Aero-Propulsion System Simulation (NASA C-MAPSS) dataset, which has modeling difficulties, including truncated RUL labels, operational regime shifts, nonlinear degradation patterns, and sensor redundancy, which makes conventional modeling approaches inadequate. To overcome these obstacles, this paper proposes a novel transformer-based dual-input model (TDIM), which effectively integrates raw sensor sequences and aggregated statistical features through a parallel encoding design. The TDIM is evaluated against the state-of-the-art architectures on the NASA C-MAPSS turbofan engine degradation dataset. Experimental results demonstrate that the TDIM significantly outperforms the existing baselines, achieving an $R^2$ of 0.99 and a low root-mean-square error of 3.21 (with 99.15% prediction accuracy). Overall, the results demonstrate that transformer dual-based architecture can effectively address the inherent limitations of the dataset and significantly improve RUL estimation accuracy, paving the way for more robust and reliable predictive maintenance systems.