Liquid-propellant rocket engine health monitoring technologies: A survey, insights, and prospects

As modern launch missions demand ever higher performance, reliability, and reusability, health monitoring of liquid-propellant rocket engines (LREs) has become a cornerstone of propulsion-system safety and efficiency. This paper provides a systematic review of the theoretical foundations, core technologies, and engineering applications of fault detection and diagnosis (FDD) within LRE health monitoring systems (HMS). The discussion spans system architecture; advanced sensing and data acquisition; model-based and data-driven diagnostic algorithms; and the integration of digital twins and artificial intelligence. Recent advances highlight a rapid transition from threshold-based monitoring and post-event analysis toward intelligent, real-time, life-cycle health management, featuring multimodal sensor fusion, hybrid physics–data modeling, and predictive-maintenance frameworks. By examining documented engineering practices and representative research efforts in the United States, China, and Russia, the paper distinguishes demonstrated applications from emerging approaches and assesses their reported benefits and remaining engineering challenges. The paper also analyses current challenges — namely data quality, model generalization, interpretability, and engineering validation — and outlines future directions in autonomous, trustworthy, and adaptive health management enabled by large-scale models and digital twins. These developments are expected to markedly enhance the safety, reliability, and operational economics of next-generation liquid rocket engines.