Physics-Informed Orthogonal Dynamics Network for Remaining Useful Life Prediction of Machinery

Deep learning has been widely applied to the prediction of the Remaining Useful Life (RUL) of machinery, achieving promising results. However, existing methods often focus on features extracted from single-directional signals and overlook the dynamic interactions among multi-directional signals, resulting in limited generalization. Moreover, purely data-driven models lack physical interpretability and often fail to reflect the underlying degradation dynamics. To address these issues, this paper proposes a physics-informed orthogonal dynamics network (PINN-OD) for RUL prediction of machinery. In the proposed method, an orthogonal dynamic coupling module is first designed to extract and fuse degradation features from multi-directional vibration signals using both homogeneous and heterogeneous convolutional branches. Then, a physics-informed module learns an implicit degradation partial differential equation (PDE) from data, enabling physical consistency through automatic differentiation. The proposed approach is validated on a run-to-failure bearing dataset under multiple operating conditions. Experimental results show that PINN-OD outperforms traditional data-driven methods in both prediction accuracy and generalization capability.