Aging-Informed Dynamic Graph Network for Aero-Engine RUL Prediction

Remaining useful life (RUL) prediction for safety-critical systems, such as aero-engines, is a fundamental challenge in prognostics and health management. While existing deep learning methods have achieved success in modeling spatiotemporal patterns, they often overlook the physical essence of system degradation: the evolution of intersensor dependencies is intrinsically driven by the aging progression. To address this, we propose aging-informed dynamic graph network for RUL prediction (AIDGN-RUL), which formulates RUL prediction as an aging-guided dynamic graph construction problem. Specifically, the model derives an aging representation from the operational cycle count and integrates it with multivariate sensor features to generate adaptive graph structures that evolve with system aging. A graph convolutional architecture is then employed to propagate information over aging-guided graph structures to obtain refined sensor representations for RUL prediction. Extensive experiments on the CMAPSS and N-CMAPSS datasets demonstrate that AIDGN-RUL significantly outperforms state-of-the-art baselines, achieving a maximum improvement of 27.83% in prognostic score and 15.94% in root mean square error (RMSE). Furthermore, the model exhibits superior robustness to sensor noise and maintains high computational efficiency, highlighting its potential for real-world industrial deployment.