Propagation and evolution graph method embedded with physical constraints for multi-factor coupled deep fault diagnosis in aero-engines

The performance of aero-engine gas path components exhibits high correlation and strong coupling. Performance degradation and faults have a significant impact on the safety and reliability of aircraft. Previous research primarily focused on establishing direct mapping relationships between measurable parameters and fault categories or performance, while neglecting the propagation and evolution mechanisms of multi-factor coupling in fault information. This paper proposes a propagation and evolution graph method, utilizing the gas path structure and fault information propagation laws as physical constraints, to construct fault propagation subgraphs and whole life cycle (WLC) fault evolution graphs, thereby enabling multi-indicator deep fault diagnosis. Firstly, based on parameter correlation analysis, a three-layer multi-factor coupling representation of fault sources, performance parameters, and measurement parameters is determined, and a symbolic directed graph method is used to construct single-condition point fault propagation subgraphs, revealing the information propagation laws of different faults. Then, a multi-dimensional fusion distance is established to achieve a WLC large-scale fusion graph for tracking the fault evolution process. Thereby, deep fault diagnosis of multiple indicators such as fault state, fault mode, and fault components is realized. Finally, various comparative experiments are designed, fully validating that the proposed method improves the accuracy and interpretability of multi-indicator fault diagnosis, providing an important basis for subsequent research on graph theory.