Journal Article

A Hybrid Model Based on a Dual-Attention Mechanism for the Prediction of Remaining Useful Life of Aircraft Engines

He, Chenwen, Zhang, Zikai, Zheng, Chenyu, Zhang, Liping, Li, Zixiang

2025

Overview

Estimating the Remaining Useful Life (RUL) of aircraft engines plays a vital role in the field of prognostics and health management. In multi-dimensional time series regression tasks, accurately capturing both time series features and sensor features, as well as integrating these two types of features, poses a significant challenge for RUL prediction. The sensor features represent the weights of each sensor on the RUL prediction results. To overcome this challenge, we introduce a hybrid model based on a dual-attention mechanism. Initially, a temporal feature extraction block is applied to map the time-step dimension into a hidden representation space, facilitating the capture of complex temporal dynamics. These patterns are then refined using a multi-head self-attention mechanism. Subsequently, a sensor feature extraction block is applied to capture sensor-specific characteristics. Each sensor sequence is treated as a separate channel, compressed to derive sensor weights, and integrated to form global features that fuse temporal and sensor-level representations. Finally, RUL is estimated via a regression layer. The proposed method is demonstrated to be effective on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Compared with the state-of-the-art CTNet model, the proposed method achieves 7% and 9% gains in RMSE and Score, respectively, on the FD001 dataset.

• Publisher: MDPI AG,Multidisciplinary Digital Publishing Institute (MDPI)
• Subject: attention mechanism / Design / Information storage / Learning / Neural networks / remaining useful life prediction / sensor feature extraction block / Sensors / temporal feature extraction block / Time series / transformer / Useful life