Remaining useful life with self-attention assisted physics-informed neural network

Remaining useful life (RUL) prediction as the key technique of prognostics and health management (PHM) has been extensively investigated. The application of data-driven methods in RUL prediction has advanced greatly in recent years. However, a large number of model parameters, low prediction accuracy, and lack of interpretability of prediction results are common problems of current data-driven methods. In this paper, we propose a Physics-Informed Neural Networks (PINNs) with Self-Attention mechanism-based hybrid framework for aircraft engine RUL prognostics. Specifically, the self-attention mechanism is employed to learn the differences and interactions between features, and reasonably map high-dimensional features to low-dimensional spaces. Subsequently, PINN is utilized to regularize the end-to-end prediction network, which maps features to RUL. The RUL prediction framework termed AttnPINN has verified its superiority on the Commercial Modular AeroPropulsion System Simulation (C-MAPSS) dataset. It achieves state-of-the-art prediction performance with a small number of parameters, resulting in computation-light features. Furthermore, its prediction results are highly interpretable and can accurately predict failure modes, thereby enabling precise predictive maintenance.