Spatial dependency learning for image-based anomaly detection in engine combustion

Traditional scramjet anomaly detection methods are constrained by delayed pressure responses and handcrafted features that depend on expert experience. To address this issue, this paper proposes an intelligent situational awareness algorithm for engine anomaly detection based on chemiluminescence imaging of combustion processes. The model learns the spatial dependencies of local features in stable flame images, using a self-supervised learning framework to characterize the feature distribution of normal image patches and identify anomalies as deviations from this distribution. Experimental results demonstrate that the proposed method achieves 100.0% accuracy and 100.0% area under the receiver operating characteristic curve (AUROC) at the image level, while 90.9% accuracy and 94.8% AUROC at the pixel level. The algorithm is trained solely on normal images and is capable of simultaneously detecting both abnormal states and abnormal regions.