Multiphysics synergistic optimization for heterogeneous gear systems: a spatiotemporal-decoupled AI framework for integrated diagnosis and design

Heterogeneous gear fault vibration signals are often influenced by the coupling of multi-level modulation sources, which severely hinders the separation of informative features and obstructs accurate tracking of their intrinsic temporal evolution. Moreover, current research in fault diagnosis lacks effective co-modeling strategies that can jointly characterize the multi-source coupling mechanisms and their dynamic progression, making it difficult to decouple fault-relevant patterns from interfering components in such modulated signals.To address this issue, we propose a physics-guided spatiotemporal decoupling framework, which integrates spatiotemporal modeling with the underlying physical modulation mechanisms. First, inspired by the kinematic characteristics of gear systems, a heuristic selective decision solver is designed to extract modulation patterns dominated by rotational periodicity, thereby achieving effective isolation of multi-source interference. Next, the extracted patterns are transformed into physically meaningful enhanced representations via a spatiotemporal encoder, which simultaneously preserves the local structural details and temporal evolution of fault features. Furthermore, a spatiotemporal coupling capture network is developed, incorporating a physically salient attention mechanism to adaptively emphasize critical fault-related components, significantly improving the discriminative capability of the learned features. Finally, a neural operator-based classifier is employed to accomplish robust fault recognition. Experimental results demonstrate that the proposed method achieves high diagnostic accuracy and robustness across a variety of complex operating conditions, enabling efficient and reliable fault diagnosis for gear transmission systems.