关键词: 重复使用, 液体火箭发动机, 尾焰光谱, 故障诊断, 定量反演, 人工智能

Abstract: With the growing engineering demand for reusable liquid rocket engines, ensuring stable and safe operation under extreme conditions—high heat flux, high chamber pressure, and violent combustion—has become increasingly challenging. Plume spectroscopic diagnostics, featuring non-contact measurement, high sensitivity, and multi-parameter sensing capability, has emerged as an important technical route for engine health monitoring and fault identification. This paper first analyzes the key challenges in liquid rocket engine development and the diagnostic requirements for fault monitoring. The diagnostic mechanism based on atomic emission spectroscopy is then systematically elaborated, followed by a review of domestic and international research progress and the current state of the art in plume spectroscopy. Next, the core enabling technologies are summarized, including the construction of a spectrum–material–fault-mode database, controlled metal-impurity doping combustion tests, quantitative inversion of alloy species concentrations in the plume, flight-environment spectral diagnostics, and spectral-line interference and mitigation strategies for LOX/kerosene engines. The applicability, accuracy, and engineering feasibility of three representative measurement techniques—Fabry–Pérot interferometry, Fourier-transform infrared spectroscopy, and laser-induced breakdown spectroscopy—are further evaluated and compared for plume spectral acquisition. Finally, future trends are discussed, with emphasis on multimodal data fusion, artificial-intelligence-enabled analysis, on-chip spectroscopy combined with edge computing, and extensions toward the near-/mid-/far-infrared and terahertz bands, highlighting the broad prospects of plume spectroscopy for intelligent operation and predictive maintenance of liquid rocket engines.

Key words: reusable, liquid rocket engine, exhaust plume spectroscopy, fault diagnosis, quantitative inversion, artificial intelligence