MSEvoNet: Multiscale Spatiotemporal Evolution Networks for Remaining Useful Life Prediction in Complex Industrial Systems

This article introduces a multiscale spatiotemporal evolution network (MSEvoNet), a multiscale spatiotemporal evolution network that analyzes complex industrial systems’ dynamic changes during service, focusing on predicting the remaining useful life (RUL). By integrating a gated dilated convolution (GDC) module, an adaptive relationship perception (ARP) module, and a diffusion graph convolution (DGC) module in a step-by-step stacking approach, MSEvoNet effectively enhances heterogeneous data management, spatiotemporal fusion, and model interpretability in complex systems, providing deeper insights into industrial system degradation. The adopted spatiotemporal graph structure portrayed the evolutionary process of complex industrial systems, depicting the intricate interactions and influences among spatiotemporal variables during degradation and the intrinsic conduits governing RUL factors, thereby constructing an explainable artificial intelligence (XAI) model for complex industrial systems. Validated with the C-MAPSS and N-CMAPSS datasets, representing complex aerospace engines’ systems, MSEvoNet demonstrated robustness and adaptability across diverse operational scenarios, significantly advancing the potential for RUL prediction. The findings underscore the importance of leveraging XAI and multiscale spatiotemporal insights for RUL prediction, offering a pathway toward more reliable and efficient prognostics for complex industrial systems in the Industry 4.0 and 5.0 era.