Adaptive Physics-encoded Graph Neural Network for Health Stage Assessment of Liquid-propellant Rocket Engines

Abstract The improvement of reliable health monitoring system (HMS) for liquid-propellant rocket engines (LREs) is a crucial part for reusable launch vehicle, which contributes to providing competitive and cost-effective propulsion systems. Thus, it accentuates the need for reliable and quick health stage assessment of system and follow-up damage-mitigating control. In this paper, we propose a novel adaptive physics-encoded graph neural network (APGNN) for health stage assessment of LREs. Our approach embeds the relations of different sensors obtained through expert experience, which contributes to constructing a physical graph layer. To better capture the information contained in all the sensor data, a novel convolutional layer of adaptive auto-regressive moving average (ARMA) filters is designed, which considers the personalized information propagation needs of each neural network layer. The performance of the proposed method is quantified with data obtained from physics simulations and real-world engineering systems. The results show that our model has potential applicability for the health stage assessment of LREs with high accuracy.