Knowledge distillation-optimized two-stage anomaly detection for liquid rocket engine with missing multimodal data

Anomaly detection (AD) is essential to ensure safe and reliable operation of liquid rocket engine (LRE). However, with harsh and complex operating conditions in LRE, existing methods find it difficult to fuse missing multimodal data and extract features for AD. To recover missing data and achieve effective AD for LRE, we propose a knowledge distillation-optimized two-stage AD method, which consists of two models: teacher model and student model. Specifically, the teacher model includes two complex modules: imputation and reconstruction module, which respectively impute and reconstruct missing multimodal data. Meanwhile, the simple student model is proposed to learn the knowledge of the teacher model, which could quickly and accurately determine the health status of LREs. In training process, the two modules of teacher are trained by two steps, and the third step is to transfer knowledge of pretrained teacher model to the student model. Finally, a high-performance and simple-structure student model is obtained. To verify the accuracy and efficiency of the proposed method, we carry out sufficient experiments and discussions to research it in many aspects with data from static firing tests. Experimental results show that F1 Score can reach 0.9916 with a delay less than 19Â ms.