A Novel Digital Twin Framework for Aeroengine Performance Diagnosis

Abstract

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.