Spatiotemporal Remaining Useful Life Prediction Using Bayesian Graph Neural Differential Models

The aircraft engine is a core component of the airplane, and its operational condition is directly related to the safety of both the equipment and personnel. Accurate Remaining Useful Life (RUL) prediction can help identify potential failure risks in advance, prevent unexpected breakdowns, and ensure operational safety throughout the flight. This paper presents a novel approach for predicting the RUL of aircraft engines using deep Bayesian graph neural ordinary differential equations. The method integrates dynamic graph structure learning to capture time-varying sensor dependencies, Bayesian inference, and neural differential equations for continuous-time modeling of spatiotemporal characteristics. Uncertainty quantification is a central focus, employing Bayesian techniques and Monte Carlo Dropout to distinguish and estimate both epistemic model and aleatoric data uncertainty. Significantly improving the reliability of uncertainty estimates. Experiments conducted on the C-MAPSS dataset demonstrate the effectiveness of the approach. The model achieves optimal prediction accuracy with a specific input sequence length, leading to well-calibrated confidence intervals with coverage rates close to nominal levels. This methodology offers enhanced RUL prediction accuracy and robust uncertainty quantification, providing valuable support for predictive maintenance decisions.