TMR-Life Prediction of Aero-Engine Gas Path

This study proposes a system for health monitoring and remaining useful life (RUL) prediction of aviation engine gas path that integrates digital twins and deep learning. Addressing the limitations of existing data-driven methods in feature extraction capabilities and dynamic interaction mechanisms, this study designs an improved Transformer model (TMR). By incorporating learnable position encoding, serial multi-head attention mechanisms, and an optimized multi-layer perceptron (MLP) structure, the model significantly enhances its ability to model time-series data and improve prediction accuracy. Based on this, a multi-level virtual-reality interaction mechanism was constructed to achieve real-time linkage between the physical space, health assessment module, and maintenance decision-making scenarios, enhancing the integration capabilities of state perception, information transmission, and collaborative decision-making. At the same time, a three-dimensional visualization interaction framework for digital twin systems was developed to improve the system's observability and interpretability.