Aeroengine intelligent gas path simulation and diagnosis based on feature fusion and meta learning

Abstract With the rapid development of artificial intelligence technology, data-driven methods show great potential in the simulation and fault diagnosis of complex industrial systems. However, the application of traditional intelligent methods in aeroengine health management still faces many challenges, including the limited number of initial fault samples and the insufficient ability to capture gas path performance degradation. To solve these problems, this paper proposed a new dual cross-modal feature fusion module and a new model-agnostic meta-learning module to construct an intelligent simulation and diagnosis framework for engine gas path. This framework can effectively integrate physical model and sensor measurement data, and realize high-precision simulation under gas path performance degradation and new fault diagnosis under small sample condition. Through the example verification and comparative analysis, the results show that under the condition of performance degradation, the simulation error of gas path parameters is only 0.074%; in the case of limited initial fault samples, the fault diagnosis accuracy is as high as 99.4%, which is significantly better than the existing intelligent methods. The research shows that the proposed intelligent simulation and diagnosis framework can provide strong support for the aeroengine intelligent health management system.