High-fidelity feature fusion and efficient identification module for real-time monitoring of scramjet combustion stability

As the core propulsion system of supersonic vehicles, the scramjet engine experiences unstable combustion phenomena in the combustor under high-speed operating conditions, which can lead to performance degradation and structural damage. Therefore, the development of supersonic flame stabilization structure identification technology is urgently needed. A Heterogeneous Feature Fusion Module (HFFM) is proposed to achieve nonlinear and organic fusion of heterogeneous data. Flame Structure Data (FSD) characterize key flame features, while Combustor Wall Pressure Data (CWPD) supplement the missing flame structure features in FSD, generating Flame Heterogeneous Feature Fusion Data (FHFFD). Additionally, a Supersonic Flame Stabilization Identification Module (SFSIM) is proposed, which combines a horn-shaped convolutional neural network with a Simplified Low Latent Transformer (SLLT) to enable dynamic and adaptive multi-scale integration of flame stabilization structure features. Experimental results indicate that HFFM effectively extracts and consolidates stable flame structure features within FHFFD during the training phase, demonstrating the ability to generalize key physical principles from FHFFD. SFSIM achieves a recognition accuracy of 97.09 % through parameter optimization and attention-based dimensionality reduction. The low latent space improves training efficiency by 10.3 %, while its parameter count accounts for only 29.73 %. While maintaining high accuracy, this approach provides efficient and robust technical support for real-time monitoring of supersonic combustion.