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. We first analyze 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.

To accurately measure the three-dimensional deformation of the structure in engine static and swing tests, we propose a high-precision displacement measurement method based on stereo vision and circular markers. This method involves affixing reflective circular markers onto the engine structure surface and capturing a sequence of images during the structural deformation process. Firstly, the Sobel edge detection algorithm is used to extract the pixel-level edges of each circular marker. Then, an interpolation-based sub-pixel edge detection method is employed to obtain sub-pixel edge points, enabling more precise localization of the markers. Finally, the elliptical least squares fitting is applied to obtain the edge contours of the markers, thereby achieving accurate positioning. To evaluate the performance of this method, we conducted displacement measurements on stationary markers placed on a vibration isolation platform. When the camera was positioned 0.6 meters away from the markers, the standard deviations of in-plane displacements in two directions were found to be 0.36 μm and 0.32 μm, respectively, while that of out-of-plane displacement was 0.58 μm. Additionally, by using a displacement stage to provide standard displacements, we further validated the applicability of the algorithm in measuring the structure displacements with inclined surfaces at a 60° angle to the camera plane, as well as ∅12 mm small-diameter pipelines. The proposed visual measurement system was applied in a static test of an engine frame, yielding satisfactory results. The experiments demonstrated that the algorithm could track the displacements of 29 circular markers in real time, with a maximum error of 5.6% compared to an inductive displacement sensor. In comparison to traditional contact-based displacement testing methods, the visual testing method offers numerous advantages, including rapid setup, low cost, high measurement accuracy, and the ability to increase the measurement quantity without significantly increasing workload, making it a reliable and effective alternative to traditional displacement testing methods.