Aero-engine remaining useful life prediction using a TCN prognostic model enhanced with dual-dimensional fusion attention

Abstract Remaining useful life (RUL) prediction is an essential technique in the prognostics and health management of aero-engines, crucial for ensuring reliability and safety. Recently, data-driven methods have achieved notable progress in aero-engine RUL prediction. However, they often neglect the impact of dimensional information and the coupling information between dimensions, failing to meet the requirements of long-term prediction tasks for aero-engines. This paper presents a prognostic model for aero-engine RUL prediction, utilizing a temporal convolutional network (TCN) enhanced by dual-dimensional fusion attention to tackle these challenges. Initially, the feature attention module is employed to weight the data from various engine sensors, thereby emphasizing key features. Subsequently, the TCN learns the temporal dependencies from the weighted input data. Additionally, a dual-dimension fusion attention module is designed to account for interactions between different dimensional data of the engine. This module extracts features from various sensors and time steps through parallel sub-structures and captures the coupling information between dimensions using an attention fusion unit, achieving inter-dimensional correlation of aero-engine data. Finally, the proposed model’s effectiveness was verified using the widely recognized C-MAPSS dataset. The results demonstrate that it outperforms state-of-the-art methods in aero-engine RUL prediction accuracy, confirming its superiority.