南京航空航天大学学报（英文版）2025，Vol.42Issue(3)：368-384,17.DOI:10.16356/j.1005-1120.2025.03.008
基于注意机制特征融合的进气道故障诊断
Inlet Fault Diagnosis Based on Attention Mechanism Feature Fusion
摘要
Abstract
To tackle the instability fault diagnosis challenges in wide-speed-range supersonic inlets,this study proposes an inlet fault decision fusion diagnosis algorithm based on attention mechanism feature fusion,achieving efficient diagnosis of instability faults across wide-speed regimes.First,considering the requirement for wall pressure data extraction in mathematical modeling of wide-speed-range inlets,a supersonic inlet reference model is established for computational fluid dynamics(CFD)simulations.Second,leveraging data-driven modeling techniques and support vector machine(SVM)algorithms,a high-precision mathematical model covering wide-speed domains and incorporating instability mechanisms is rapidly developed using CFD-derived inlet wall pressure data.Subsequently,an inlet fault decision fusion diagnosis method is proposed.Pressure features are fused via attention mechanisms,followed by Dempster-Shafer(D-S)evidence theory-based decision fusion,which integrates advantages of multiple intelligent algorithms to overcome the limitations of single-signal diagnosis methods(low accuracy and constrained optimization potential).The simulation results demonstrate the effectiveness of the data-driven wide-speed-range inlet model in achieving high precision and rapid convergence.In addition,the fusion diagnosis algorithm has been shown to attain over 95%accuracy in the detection of instability,indicating an improvement of more than 5%compared to the accuracy of other single fault diagnosis algorithms.This enhancement effectively eliminates the occurrence of missed or false diagnoses,while demonstrates robust performance under operational uncertainties.关键词
宽速域超声速进气道/数据驱动建模/注意力机制/Dempster-Shafer证据理论/故障诊断Key words
wide-speed-range supersonic inlet/data-driven modeling/attention mechanism/Dempster-Shafer(D-S)evidence theory/fault diagnosis分类
航空航天引用本文复制引用
张晓乐,肖玲斐,刘金超,韩子瑞..基于注意机制特征融合的进气道故障诊断[J].南京航空航天大学学报（英文版）,2025,42(3):368-384,17.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(No.62373185)and the National Key R&D Program of China(No.2023YFB3307100). （No.62373185）
