The remaining useful life (RUL) of complex mechanical systems is the primary aspect of prognostics and health management, which is critical for ensuring reliability and safety. Recent developments have shifted towards a data-driven approach, emphasizing empirical insights over expert opinions. The similarity-based data-driven approach operates on the premise that systems with similar historical behaviors will likely exhibit similar future behaviors, making it suitable for RUL estimation. Conventionally, most similarity-based approaches utilize all historical data to identify reference systems for RUL estimations. However, not all historical events within a system hold equal significance for RUL. Certain events have a substantial impact on the remaining lifespan of a system. These significant and impactful events are called degradation events (DEs) in this study. Based on the hypothesis that systems undergoing similar DEs may share the same RUL, this study presents an innovative framework for RUL estimation that leverages only the DEs of a test system to identify reference systems that have experienced similar DEs. Furthermore, the model incorporates novel strategies for adjusting the RUL of the reference system based on the initial wear and degradation rates, thereby improving estimation accuracy. The effectiveness of the proposed model, in comparison with similar state-of-the-art models, is demonstrated through experiments on widely recognized jet engine datasets provided by NASA and bearing degradation data from the XJTU-SY.

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.